COGNITIVE PATHWAYS: FROM LANGUAGE PROCESSING
TO PEDAGOGICAL PRACTICE
by
Leah Chambers
B.Ed. & M.Ed., Saint Petersburg State University, 2004

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
PSYCHOLOGY

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
July 2025
© Leah Chambers, 2025

Abstract
This dissertation examines cognitive mechanisms underlying learning and evaluates
strategies to optimize educational outcomes across three domains: single-word processing,
flashcard-based learning strategies, and the integration of generative artificial intelligence
(GenAI) tools in postsecondary education.
In the first investigation, a mixed factorial experimental design assessed whether
recurrent childhood otitis media produced lasting phonological processing deficits in young
adulthood. Participants completed a degraded speech word recognition task with varying
phonological congruency while group differences were analyzed via mixed factorial ANOVA.
Results revealed significant effects of congruency on word recognition accuracy but no residual
phonological deficits attributable to early otitis media, suggesting the operation of compensatory
cognitive mechanisms.
The second investigation comprised three experiments evaluating flashcard-based
learning strategies. Using between-subjects designs, retrieval practice, spaced practice, and the
elaborative Flashcards-Plus method were compared in both paper-based and digital formats.
Outcomes measured included test performance, Judgment of Learning (JOL) accuracy, test
anxiety, and cramming tendencies. Across experiments, structured retrieval practice and
elaborative encoding promoted superior retention, enhanced metacognitive monitoring, and
decreased reliance on cramming compared to traditional study methods.
The third investigation explored the application of GenAI tools in education.
Experimental studies evaluated how AI-assisted learning influenced cognitive load, learning
efficacy, metacognitive engagement, and test anxiety. Students critically assessed AI-generated
explanations and writing samples, and quantitative analyses demonstrated that structured AI use

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improved learning outcomes and academic confidence while underscoring the need for AI
literacy to prevent shallow cognitive processing.
Collectively, the findings contribute to cognitive psychology, educational research, and
the Scholarship of Teaching and Learning (SoTL) by elucidating the adaptability of cognitive
systems, the efficacy of active retrieval-based study strategies, and the pedagogical opportunities
and challenges presented by emerging AI technologies in higher education.

iii

Table of Contents
Abstract ........................................................................................................................................... ii
Table of Contents ........................................................................................................................... iv
List of Tables.................................................................................................................................. ix
List of Figures ..................................................................................................................................x
Dedication ...................................................................................................................................... xi
1. Introduction: Exploring Diverse Aspects of Cognitive Mechanisms in Learning ...................... 1
2. Cognitive Mechanisms of Single Word Processing .................................................................. 13
Theories of Reading .................................................................................................................. 14
Orthographic Processing and Visual Word Recognition....................................................... 18
Phonological and Dual-Route Models of Reading ................................................................ 19
Connectionist Models............................................................................................................ 23
Cognitive and Neural Mechanisms in Word Recognition ..................................................... 25
Effects of Recurrent Childhood Otitis Media on Reading in Young Adulthood ...................... 27
Significance of Phonological Awareness .................................................................................. 28
Long-lasting Effects of Otitis Media ......................................................................................... 32
Experiment ................................................................................................................................ 33
Method .................................................................................................................................. 33
Participants ........................................................................................................................ 33
Materials ............................................................................................................................ 34

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Design and Stimuli .........................................................................................................34
Software and Presentation ..............................................................................................35
Procedure............................................................................................................................36
Ethics Approval ......................................................................................................................37
Data Analysis .............................................................................................................................37
Results ....................................................................................................................................37
Discussion ..................................................................................................................................39
Limitations of the Study .............................................................................................................42
Future Directions ........................................................................................................................ 42
Conclusions ................................................................................................................................43
3. Optimizing Postsecondary Learning Outcomes with Flashcard-Based Strategies .....................46
Retrieval Practice, Spaced Practice, and Their Digital Applications .........................................49
Elaborative Encoding and the Flashcards-Plus Strategy ............................................................53
Modality Effects .........................................................................................................................55
JOLs ...........................................................................................................................................56
Test Anxiety ...............................................................................................................................57
Research Objectives ...................................................................................................................59
Ethics Approvals ........................................................................................................................61
Experiment 1: Spaced Practice vs. Retrieval Practice ................................................................63
Method ...................................................................................................................................63

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Participants .........................................................................................................................63
Procedures ..........................................................................................................................64
Results ....................................................................................................................................66
Test Scores .........................................................................................................................66
Test Anxiety .......................................................................................................................69
JOLs ...................................................................................................................................70
Cramming Tendencies........................................................................................................70
Experiment 2: Flashcards-Plus vs Traditional Flashcards in Paper-Based Format....................72
Method ...................................................................................................................................72
Participants .........................................................................................................................72
Materials .............................................................................................................................73
Procedures ..........................................................................................................................73
Results ....................................................................................................................................76
Test Scores .........................................................................................................................76
Test Anxiety .......................................................................................................................77
JOLs ...................................................................................................................................78
Experiment 3: Flashcards-Plus in Digital Format ......................................................................79
Method ...................................................................................................................................79
Participants .........................................................................................................................79
Materials .............................................................................................................................81
Procedures ..........................................................................................................................81
Results ....................................................................................................................................84
Conclusions ................................................................................................................................85
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Discussion ................................................................................................................................. 87
Implications and Future Directions ........................................................................................... 90
4. Exploring the Efficacy of GenAI Tools in Post-Secondary Education ..................................... 95
Test anxiety ............................................................................................................................... 97
Cognitive Load, Active and Authentic Learning ...................................................................... 98
Educational use of GenAI ......................................................................................................... 99
Method .................................................................................................................................... 101
Research Site and Participants............................................................................................. 102
Data Collection Procedures ................................................................................................. 102
Ethics Approvals ..................................................................................................................... 105
Data Analysis .......................................................................................................................... 106
Results ..................................................................................................................................... 107
Efficacy of Learning............................................................................................................ 107
Examining Test Anxiety and JOLs ..................................................................................... 109
Discussion ............................................................................................................................... 110
Limitations of the Study .......................................................................................................... 114
Future Directions ..................................................................................................................... 114
Conclusions ............................................................................................................................. 116
5. Conclusion: Advancing Learning Through Cognitive Research, Established Practices, and AI
Tools............................................................................................................................................ 117

vii

References ....................................................................................................................................133
Appendix 1. Ethics Approval E2022.0107.001.01 .......................................................................151
Appendix 2. Ethics Approval 6009020 ........................................................................................ 152
Appendix 3. Ethics Approval EO-2024-0508-102. ......................................................................153
Appendix 4. Flashcards ................................................................................................................ 154
Appendix 5. Ethics Approval 6009220 (2) .................................................................................. 170

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List of Tables
Table 1. Experiment Demographics .............................................................................................. 38
Table 2. Experiment 1 Demographics ........................................................................................... 64
Table 3. Means, standard deviations, and results of MANOVA models for Quizlet and Cram
groups ........................................................................................................................................... 67
Table 4. Mann-Whitney U Test Results with Medians and Interquartile Range (IQR) for
flashcard users and non- users ....................................................................................................... 69
Table 5. L1 and L2 by Institution… .............................................................................................. 81

ix

List of Figures
Figures 1 and 2. Dual-route Theories of Reading… ......................................................................20
Figure 3. Dual-Route Model of Visual Word Recognition… ........................................................ 22
Figure 4. Estimated Marginal Means .............................................................................................39

x

Dedication
To Devon and Eliyanah — for your love, patience, and the joy you bring to every step of
this journey.

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1. Introduction: Exploring Diverse Aspects of Cognitive Mechanisms in Learning
How does the human mind navigate the complexities of acquiring, processing, and
applying knowledge? From recognizing patterns in sensory input to constructing advanced
conceptual frameworks, cognitive mechanisms form the foundation of our ability to learn, adapt,
and innovate. These mechanisms drive essential processes such as attention, memory, problemsolving, and decision-making, enabling us to understand and interact with the world. Learning is
not a singular, uniform process but rather a multifaceted interaction of cognitive functions, each
contributing uniquely to knowledge acquisition, retention, and application (Anderson &
Krathwohl, 2001). This dissertation investigates the underlying cognitive mechanisms of singleword processing, examines the efficacy of memory-enhancing learning strategies, particularly
flashcards and retrieval-based methods, and explores the role of GenAI in education. Through a
series of experiments, the research aims to provide empirical insights into how these domains
support human learning and adaptation.
Among these cognitive functions, single-word processing is fundamental to reading and
language comprehension, engaging orthographic, phonological, and semantic mechanisms.
Chapter 2 of this dissertation focuses on single-word processing, examining how individuals
recognize and interpret words through orthographic and phonological processing. It highlights
how phonological activation occurs even in silent reading, as supported by recent cognitive and
neuroimaging research (Perfetti & Helder, 2022; Seidenberg et al., 2022). This chapter draws on
Stanovich’s (1980) interactive-compensatory model, which conceptualizes reading as an
adaptive and interactive process. Within this framework, cognitive subcomponents such as
orthographic, phonological, and semantic processing operate flexibly and in coordination, so that
strengths in others can compensate for weaknesses in one area. This perspective supports the
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investigation of how individuals with early auditory disruptions may achieve reading fluency
through reliance on alternative pathways.
In addition to reviewing theoretical models and empirical findings, this chapter includes
an experimental study that explicitly evaluates the long-term impact of early auditory deficits,
specifically recurrent otitis media, on phonological activation during silent reading in young
adults. By integrating cognitive theory with experimental data, the chapter explores how early
disruptions in auditory input may influence the development of phonological awareness and
reading ability and whether individuals with a childhood history of otitis media exhibit long-term
phonological processing differences in adulthood, offering insights into cognitive compensatory
mechanisms. These questions are further contextualized through the lens of the interactivecompensatory model (Stanovich, 1980) that suggests that even in the presence of early
phonological deficits, readers may adapt by increasingly relying on orthographic or semantic
information to support fluency. In parallel, the Matthew Effect (Stanovich, 2009) highlights how
early delays in reading development, if unaddressed, may lead to widening gaps in literacy and
cognitive outcomes. Together, these frameworks provide a dual lens for interpreting whether the
effects of early auditory disruption are sustained, compensated for, or eliminated over time.
Research on word recognition highlights the interconnected roles of orthographic,
phonological, and semantic processing within visual word recognition. In this context, the
current work specifically refers to orthographic processing. Various theoretical models, including
those proposed by Brysbaert (2022) and Perfetti and Helder (2022), offer distinct perspectives on
how these cognitive processes interact to support reading and comprehension. Theories of
reading, such as the dual-route model (Coltheart, 2006) and connectionist models (Seidenberg et
al., 2022), provide competing explanations for how individuals recognize written words. The

2

dual-route model suggests that readers rely on two pathways: a lexical route that allows for direct
word recognition and a phonological route that involves letter-to-sound decoding. In contrast,
connectionist models propose a probabilistic learning mechanism, where neural networks
dynamically adjust to linguistic input, strengthening connections between orthographic,
phonological, and semantic representations through experience.
The scientific exploration of reading dates back more than a century, with early
contributions from James (1890), who examined perception, consciousness, and how the mind
interprets written symbols. While James acknowledged the role of both automatic and conscious
processes in reading, it was Huey (1908) who first provided an in-depth analysis of word
recognition, eye movement patterns, the role of prior knowledge in reading comprehension, and
the application of such research to an educational context. This early foundation has since
evolved into contemporary models of reading that emphasize the interactive nature of word
recognition. The Reading Systems Framework (Perfetti & Helder, 2022) underscores how
orthographic, phonological, and semantic components interact dynamically, influencing reading
efficiency. The Visual Word Form Area (VWFA), located in the left fusiform gyrus, has been
identified as playing a crucial role in recognizing written words by linking orthographic patterns
with phonological and semantic information (Perfetti & Helder, 2022). Event-related potential
(ERP) studies indicate that phonological activation occurs within 200 milliseconds of word
presentation, even in silent reading, reinforcing the view that orthographic and phonological
processing are tightly coupled (Seidenberg et al., 2022).
Beyond general reading models, research has examined how early auditory deficits can
disrupt phonological processing and, consequently, reading development. Otitis media, a
common childhood condition, interferes with consistent auditory input during phoneme

3

acquisition, potentially leading to weaker phonological representations, mental codes for
language sounds, and subsequent reading difficulties (Mody et al., 1999). These findings are
consistent with early work by Petinou et al. (2001), who reported that children with a history of
otitis media demonstrated significantly poorer phonological and morphophonological perception
compared to their peers without such histories. Their results provide empirical evidence that
transient hearing loss in early childhood can disrupt the formation of phonological categories,
which are foundational for later reading and language skills.
Since phonological awareness, the ability to recognize and manipulate spoken
components of words, is a key predictor of literacy outcomes (Carroll & Breadmore, 2018),
disruptions in early auditory experience may have lasting consequences. Experimental studies
have demonstrated that children with a history of recurrent otitis media struggle with phoneme
discrimination tasks, perform poorly in non-word reading assessments, and exhibit delayed
phonological awareness (Winskel, 2006). While some findings suggest that these deficits persist
into adulthood, others indicate that compensatory mechanisms may allow for partial recovery of
phonological skills over time (Zumach et al., 2010). This developmental vulnerability is
underscored by Stanovich’s (2009) Matthew Effect framework, which posits that early
disadvantages in reading can compound over time. Children who fall behind in phonological
decoding may encounter less print exposure, slower vocabulary growth, and reduced reading
motivation, leading to an escalating cycle of academic underperformance.
These outcomes echo the core claims of the Matthew Effect (Stanovich, 2009), which
proposes that initial differences in reading ability, especially in phonological decoding, are
magnified over time due to reciprocal relationships between reading proficiency, cognitive
development, and exposure to print. Children who develop phonological awareness early gain

4

greater access to meaningful reading experiences, which in turn accelerate vocabulary growth,
syntactic knowledge, and background knowledge. Conversely, those with early phonological
deficits are likely to experience slower reading development, reduced engagement with text, and
cumulative academic disadvantages. Understanding these cognitive mechanisms has practical
applications, particularly in designing interventions for individuals with reading difficulties.
Structured phonological training programs and adaptive AI-driven literacy tools have been
developed to reinforce grapheme-phoneme correspondences, helping mitigate phonological
deficits and improve literacy outcomes (Snowling et al., 2022).
Beyond word recognition, learning and memory play a crucial role in the acquisition and
application of knowledge, with flashcards serving as a long-established self-testing tool that
leverages retrieval practice, spaced repetition, and elaborative encoding to enhance learning.
Chapter 3 of this dissertation systematically evaluates the effectiveness of flashcard-based
learning strategies through three experiments. The first experiment compares retrieval practice
with spaced practice in digital learning environments to determine which technique optimally
supports long-term retention. The second experiment investigates the Flashcards-Plus strategy, a
structured flashcard approach that incorporates elaborative encoding, requiring students to
paraphrase definitions and generate personalized examples to enhance comprehension (Appleby,
2013; Senzaki et al., 2017). The Flashcards-Plus strategy builds on traditional flashcard use by
incorporating elaborative encoding techniques, requiring students to paraphrase definitions and
generate personalized examples, which enhances cognitive engagement and promotes deeper
learning (Appleby, 2013; Senzaki et al., 2017). The third experiment integrates the FlashcardsPlus strategy with spaced repetition algorithms in digital tools such as Anki, examining whether
this combination optimizes learning and reduces test anxiety.

5

Collectively, these studies provide empirical evidence on how structured study methods
impact student performance, self-regulated learning, and academic confidence.
Research has consistently demonstrated that retrieval-based learning strengthens memory
pathways by requiring active recall, reinforcing neural connections and reducing forgetting over
time (Carpenter et al., 2022). Spaced practice further optimizes memory consolidation by
strategically timing review sessions just before forgetting occurs (Cepeda et al., 2006). These
techniques engage the hippocampus and prefrontal cortex, reinforcing retrieval cues and
minimizing cognitive overload (Yuan, 2022). While traditional flashcards primarily support
simple memorization, structured approaches such as the Flashcards-Plus strategy have been
shown to enhance retention, comprehension, and application by engaging students in deeper
cognitive processing (Senzaki et al., 2017).
The Flashcards-Plus strategy builds on retrieval-based learning by requiring students to
paraphrase definitions and generate personalized examples, promoting deeper encoding and
metacognitive awareness (Appleby, 2013; Senzaki et al., 2017). Research supports that
elaborative encoding strengthens retrieval cues, making recall more automatic and durable
(McDaniel, 2023). Experimentally, Flashcards-Plus have been shown to improve knowledge
retention beyond traditional flashcards, particularly when combined with spaced repetition.
Digital flashcard platforms such as Quizlet (Quizlet, n.d.-a), Cram (Cram.com, n.d.-a), and Anki
(AnkiWeb, n.d.) further enhance these benefits by integrating adaptive learning algorithms that
automate spaced repetition schedules to optimize review timing (Özdemir & Seçkin, 2024;
Fursenko et al., 2021).
Despite the well-documented advantages of retrieval-based strategies, they remain
underutilized, often overshadowed by passive study methods such as rereading and highlighting,

6

which can create a false sense of mastery (Brown-Kramer, 2022). Research indicates that
structured flashcard techniques, particularly those incorporating retrieval practice and spaced
repetition, not only enhance knowledge retention but also promote deeper cognitive engagement,
fostering self-regulated learning (Dunlosky et al., 2013). Furthermore, these strategies have been
shown to mitigate test anxiety by reinforcing a sense of control over the learning process,
ultimately improving both confidence and academic performance (Rana et al., 2020).
While these strategies improve individual learning, technological advancements have
introduced new opportunities to enhance education through artificial intelligence. The role of
GenAI in postsecondary education presents a significant shift in the way students engage with
learning, offering new opportunities to strengthen study strategies, personalize instruction, and
alleviate cognitive challenges such as test anxiety and information overload (Chiu, 2024; Sheikh
et al., 2023). As AI-driven tools evolve, they continue to expand their applications in self-testing,
writing proficiency, and knowledge retention (Tahiru, 2021). However, despite their growing
presence in educational settings, empirical research on their direct impact on learning efficacy,
metacognitive awareness, and self-regulation remains in its early stages, highlighting the need
for further scrutiny of their influence on academic performance and student well-being
(Zawacki-Richter et al., 2019).
This dissertation explores the cognitive impact of GenAI in postsecondary education,
with a focus on metacognition, learning efficiency, and self-regulated study strategies. Chapter 4
examines how AI-driven tools shape student engagement with learning materials, analyzing both
their cognitive benefits and potential challenges. By investigating how students can integrate AI
into their study practices, this research identifies the conditions under which AI enhances or
impedes learning, providing insights for optimizing its role in education. Through a series of

7

experimental studies, the research evaluates the effectiveness of AI-assisted study techniques in
improving retention and knowledge application compared to traditional methods. Additionally, it
explores students’ perceptions of AI-generated content, assessing their ability to critically
evaluate, adapt, and effectively incorporate AI assistance into their academic workflows.
A central feature of GenAI in education is its ability to generate new content through
deep learning models, ranging from text and images to interactive tutoring responses that align
with user input (Chiu, 2024). Chatbots such as ChatGPT function as conversational AI assistants,
utilizing pre-trained transformer models to analyze prompts and produce human-like responses
(OpenAI, 2023). These tools have demonstrated potential in facilitating content comprehension,
supporting self-testing, and enhancing writing skill development, making them valuable
academic study aids. Research has increasingly emphasized the ability of AI-enhanced learning
environments to develop critical thinking, digital literacy, and independent study habits (Kee et
al., 2024; Muthmainnah et al., 2022).
Chapter 4 presents an experiment evaluating the effectiveness of AI-generated
explanations in improving conceptual understanding. It examines how students engage with
GenAI-integrated interactive assignments, where they critically assess AI-generated content,
cross-reference responses with reliable sources, and refine their understanding through iterative
self-directed inquiry. The study also explores students’ confidence in AI-assisted learning,
investigating whether structured dialogues with AI enhance concept clarification, reinforce
learning through retrieval practice, and deepen overall comprehension of course material. These
applications align with constructivist learning theories, emphasizing active knowledge
construction through exploration, problem-solving, and reflection (Piaget, 1952; Vygotsky,
1978).

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Beyond its role in content comprehension, another experiment in Chapter 4 explores
GenAI’s potential to support the development of writing skills and academic self-regulation. The
students in this experiment were tasked with assessing AI-generated essays using instructorprovided rubrics with the goal of enhancing students’ analytical thinking, structural awareness,
and the ability to recognize well-supported arguments.
The ability of GenAI to generate multiple writing samples allows students to compare
different writing styles, assess coherence, and refine their compositions based on structured
feedback. Despite these advantages, concerns arise regarding cognitive offloading, where
students rely excessively on AI-generated text without critically engaging with the material.
Studies on cognitive offloading suggest that over-reliance on external tools may reduce active
engagement, potentially weakening memory retention and problem-solving skills (Risko &
Gilbert, 2016). This passive interaction can weaken retention and hinder the meaningful
application of knowledge, underscoring the need for AI literacy training to ensure that students
approach AI-generated content with analytical rigour rather than unquestioning acceptance.
From a cognitive load perspective, GenAI offers both advantages and challenges. AI
tools have the capacity to reduce extraneous cognitive load by streamlining access to
information, automating repetitive tasks, and providing instant clarification of complex concepts
(Sweller, 2010). By offloading mechanical cognitive processes, students can allocate more
cognitive resources to higher-order learning activities such as critical thinking, application, and
schema development (Lombardi & Oblinger, 2007). However, the potential for over-reliance on
AI-generated content without active engagement raises concerns about shallow processing and
diminished long-term retention. Ensuring the effective implementation of AI in education

9

requires guiding students toward metacognitive awareness so that they actively interrogate,
analyze, and refine AI-generated information rather than passively accepting it.
One of GenAI's most compelling potential benefits is its role in alleviating test anxiety,
which has been identified as a significant barrier to academic success, impacting both selfefficacy and performance (Cassady & Johnson, 2002; Gerwing et al., 2015). Structured study
interventions such as self-testing, guided study strategies, and real-time feedback mechanisms
have been shown to mitigate anxiety by reinforcing students’ sense of control over their learning
(Rana & Mahmood, 2010). GenAI-powered chatbots may offer a novel approach to reducing test
anxiety by creating low-stakes, personalized learning environments that allow students to assess
their knowledge gaps and reinforce their understanding without external pressure (Tahiru, 2021).
A key research question in Chapter 4 investigates whether AI-driven study companions can
reduce test anxiety and enhance students’ confidence in their preparedness.
Although students report positive perceptions of AI-assisted study tools, research
suggests that test anxiety is influenced by broader psychological factors, including prior
knowledge, self-efficacy beliefs, and perceived academic control (Connonn et al., 2016). This
indicates that while GenAI can improve learning confidence, it may not fully address the
emotional dimensions of anxiety, such as fear of failure or test-related cognitive distortions.
Consequently, the most effective approach to integrating AI into education would combine AIassisted learning with self-regulation training and cognitive-behavioural strategies to manage
anxiety.
Despite the clear potential of GenAI to enhance educational outcomes, ethical concerns
and institutional policies remain critical considerations in its integration. Research suggests that
while students find AI-generated responses useful in understanding complex topics, both

10

students and educators express concerns about accuracy, misinformation, and the ethical
implications of AI use (Eaton, 2023; Mahmud, 2024). A significant issue is AI-generated
“hallucinations,” where chatbots produce plausible but factually incorrect responses (Chiu,
2024). To counteract this risk, students must be trained to critically evaluate AI-generated
content, cross-reference it with peer-reviewed sources, and engage in reflective analysis (Reich
& Ito, 2017). Encouraging transparent discussions about AI use and integrating AI literacy
programs into curricula can help students navigate its strengths and limitations while fostering
responsible engagement with emerging technologies.
As the role of GenAI in higher education continues to evolve, its integration requires a
balanced approach that aligns technological innovation with pedagogical rigour (Sana et al.,
2020). The findings in Chapter 4 contribute to the broader discourse on AI in education by
providing empirical evidence on its efficacy, highlighting potential pitfalls, and offering
recommendations for responsible implementation. These findings are particularly relevant to
educational and cognitive psychology, as they shed light on how AI tools influence learning
strategies, student motivation, metacognitive judgments, and perceptions of credibility and trust
in automated systems.
Building on this perspective, this dissertation examines the cognitive mechanisms that
underpin learning by exploring single-word processing, retrieval-based learning strategies such
as flashcards, and the role of AI in education. The integration of orthographic and phonological
processing in word recognition, the application of structured self-testing techniques, and the
potential of AI-driven adaptive learning highlight human cognition's intricate yet interconnected
nature. By adopting an interdisciplinary approach, this research contributes to the broader effort

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of optimizing learning strategies, improving educational outcomes, and leveraging AI-driven
tools to support knowledge acquisition and application.
Drawing from cognitive psychology, psycholinguistics, and SoTL, this work integrates
empirical findings to refine instructional methodologies and enhance learner engagement. By
examining how structured retrieval-based learning strengthens memory retention, how
orthographic and phonological processing facilitate efficient word recognition, and how AIdriven adaptive learning tools personalize instruction, this research bridges theoretical insights
with practical applications. Ultimately, these findings inform evidence-based pedagogical
strategies that support diverse learners, foster metacognitive awareness, and maximize the
effectiveness of both traditional and technology-enhanced educational approaches. This
dissertation presents three related but methodologically distinct investigations into cognitive
processes in postsecondary learners: single-word recognition, flashcard-based learning strategies,
and GenAI-supported learning.
A fundamental starting point in this exploration is the process of single-word recognition
— a cognitive function essential to reading, language comprehension, and learning at large.
Understanding how individuals perceive, decode, and interpret written words requires examining
the underlying orthographic and phonological mechanisms that support fluent reading. The next
chapter focuses on these processes, investigating theoretical models of word recognition and the
neural and cognitive pathways involved in single-word processing. The final section will present
an experimental study designed to answer the central research question, whether early auditory
disruptions impact phonological processing in young adults, and discuss its findings and
implications.

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2. Cognitive Mechanisms of Single Word Processing
Recognizing and processing single words is a fundamental aspect of human cognition,
forming the basis of reading and language comprehension. This chapter investigates the
cognitive mechanisms involved in single-word processing, with a particular focus on
orthographic and phonological processing. Specifically, it asks whether early auditory disruption
resulting from recurrent otitis media has a lasting impact on phonological awareness and reading
ability in adulthood. Drawing on theoretical models and empirical research, it explores how
individuals recognize and interpret words and examines whether early auditory disruptions
influence phonological awareness and reading ability in adulthood. Understanding the cognitive
processes involved in word recognition, particularly orthographic and phonological processing,
is crucial for clarifying how reading develops, how early auditory disruptions impact reading
ability, and how theoretical models account for individual differences.
Research on single-word processing has highlighted the interplay between visual word
recognition, phonological activation, and semantic retrieval, with different theoretical models
offering insights into how these processes interact. Recent findings suggest that phonological
activation occurs even in silent reading, influencing both word identification and meaning
retrieval (Brysbaert, 2022). Masked priming studies have demonstrated that phonological
representations are automatically activated even when the reading task does not require
pronunciation, reinforcing the importance of phonological processing in fluent reading (Perfetti
& Helder, 2022). These findings are further supported by neuroimaging evidence from
Pattamadilok et al. (2017), who demonstrated that phonological and semantic systems are
activated in a task-independent manner during visual word recognition, providing robust support
for the automaticity assumptions underlying connectionist models. Theories such as the dual13

route model (Coltheart, 2006) and connectionist models (Seidenberg et al., 2022) provide
frameworks for understanding how readers navigate the complexities of written language. A
distinction must be made between these models: the dual-route model posits separate lexical and
phonological pathways for reading, whereas connectionist models emphasize a distributed and
probabilistic approach to word recognition. Clarifying these distinctions helps contextualize
experimental findings. Additionally, studies on individuals with a history of recurrent otitis
media offer valuable insights into how early auditory deficits may affect phonological processing
and, subsequently, reading outcomes.
This chapter will first discuss major theories of reading, followed by an exploration of the
cognitive and neural mechanisms involved in word recognition. It will then review the long-term
effects of recurrent otitis media on reading ability, highlighting its implications for phonological
awareness. The final section will present an experimental study investigating whether early
auditory disruptions impact phonological processing in young adults and discuss its findings and
implications.
Theories of Reading
Reading is a fundamental skill that plays a significant role in our everyday lives. From
navigating through textbooks in schools to comprehending thought-provoking literary works,
deciphering written text is essential to our cognitive development and intellectual growth.
However, the reading process is not just a simple act of decoding letters and words; rather, it
encompasses a highly complex interaction between cognitive, linguistic, and perceptual systems
(Duke & Cartwright, 2021). As such, theories of reading seek to contribute to our understanding
of how individuals engage with written texts, their cognitive processes during reading, and the
implications for pedagogical practices.

14

The scientific exploration of reading dates back more than a century, with early
contributions from James (1890), who examined perception, consciousness, and how the mind
interprets written symbols. Although James touched on the automatic and conscious processes
involved in reading, his work lacked the experimental rigour and systematic approach seen in
later cognitive research. A more comprehensive analysis of reading was later presented by Huey
(1908), whose seminal work provided one of the first in-depth discussions of word recognition,
eye movement patterns, and the role of prior knowledge in reading comprehension. Huey’s
findings laid the foundation for modern theories of reading by demonstrating that reading is an
active cognitive process that requires attention, memory, and the integration of visual input with
linguistic knowledge rather than a simple visual recognition task.
Building upon this foundation, contemporary reading research has expanded to examine
multiple levels of text processing, ranging from word recognition to sentence and discourse
comprehension (Snowling et al., 2022). A growing body of studies highlights the dynamic
interplay between orthography, phonology, and semantics in skilled reading, demonstrating that
word identification is not an isolated process but an integral part of a broader linguistic network
(Perfetti & Helder, 2022).
At the core of reading lies the intricate coordination of cognitive processes, including
visual, phonological, and semantic processing, all of which contribute to fluent word recognition
and comprehension. The Reading Systems Framework proposed by Perfetti and Helder (2022)
provides a structured model for understanding these interactions, emphasizing that word
identification is not a linear sequence but a dynamic, interactive system. Within this system,
orthography enables recognition of printed word forms, phonology facilitates the mapping of
written symbols to spoken sounds, and semantics supports the retrieval of word meanings.

15

Rather than operating in isolation, these processes function in parallel, continuously influencing
one another to enable efficient meaning extraction from text (Grainger, 2022).
However, for a comprehensive theory of reading, evidence from word, sentence, and text
recognition and comprehension needs to be better integrated (Snowling et al., 2022). Word
recognition is a complex process that incorporates orthographic, phonological, and semantic
processing. Empirical evidence strongly supports the role of phonology in silent reading, with
studies demonstrating that phonological activation occurs automatically during word recognition
(Brysbaert, 2022; Perfetti & Helder, 2022). Theories such as the dual-route model and
connectionist frameworks offer differing perspectives on how readers navigate the complexities
of written language. Furthermore, the effects of early auditory deficits, such as recurrent otitis
media, highlight the significance of phonological awareness in reading development.
Understanding these cognitive mechanisms can contribute to more effective literacy
interventions and educational practices for individuals with reading difficulties.
Recent research underscores the importance of phonology in silent reading, suggesting
that it plays a more central role than previously assumed. Studies on bilingual readers and
individuals with dyslexia further demonstrate that phonological processing is a core aspect of
word recognition and that deficits in phonological awareness can lead to reading difficulties
(Brysbaert, 2022). This chapter builds on current reading research by first examining the
cognitive mechanisms involved in single-word processing, with a focus on phonological and
orthographic processing. It then extends into exploring whether developmental auditory
deprivation results in long-term disruptions to these processes, particularly in the efficiency and
accuracy of phonological decoding and lexical access.

16

The investigation of single-word recognition begins with one of the central debates in
reading science: how are individual words recognized during reading? While some early theories
proposed that words are identified as whole visual units, more recent evidence suggests that
letter-based processing plays a crucial role in word recognition (Grainger, 2022). The interactive
activation model, for example, suggests that visual features activate letter representations, which
then trigger whole-word recognition through a hierarchical process (Perfetti & Helder, 2022).
This model supports the idea that word recognition depends on both bottom-up processing (from
visual input to letter encoding) and top-down influences from prior linguistic knowledge (Perfetti
& Helder, 2022).
Connectionist models challenge the notion of fixed pathways in reading and instead
propose that word recognition is based on probabilistic learning (Seidenberg et al., 2022).
According to this view, neural networks dynamically adjust to linguistic input, gradually
strengthening connections between orthographic patterns and their phonological and semantic
representations through experience (Seidenberg et al., 2022). This perspective aligns with
findings that suggest reading development is shaped by exposure to print and spoken language
rather than the application of pre-defined rules.
In this chapter, several established theories of reading are discussed to provide a
foundation for understanding the cognitive processes involved in word identification and
comprehension. These theoretical frameworks are then used to guide an empirical investigation
of reading mechanisms in two distinct groups of young adults. The first group includes
individuals with a childhood history of otitis media, a condition marked by recurrent ear
infections that can interfere with auditory processing and phonological development. By
comparing this group with a control group of individuals without such a history, the study aims

17

to assess potential differences in cognitive processing and reading outcomes. Understanding
these differences may shed light on how early auditory disruptions influence the development of
reading skills and their long-term impact on language processing.
Orthographic Processing and Visual Word Recognition
One of the fundamental components of reading is orthographic processing, which allows
the mind to convert visual input into linguistic representations (Murray, 2016; Grainger, 2022).
Unlike the outdated “word shape” hypothesis, which suggested that words are recognized
holistically, contemporary research supports the letter-based recognition hypothesis. According
to this view, word recognition relies on encoding letter identities and positions rather than overall
word shape (Grainger, 2022).
Visual word recognition also depends on the Visual Word Form Area (VWFA), a
specialized neural region in the left fusiform gyrus that processes orthographic information
(McCandliss et al., 2003; Dehaene & Cohen, 2011). Eye-tracking studies have demonstrated that
skilled readers adjust their fixations to optimize word recognition efficiency, relying on parafoveal preview and rapid lexical access (Perfetti & Helder, 2022). Furthermore, computational
models of word reading have shown that probabilistic mappings between spelling, sound, and
meaning govern the efficiency of word recognition (Seidenberg et al., 2022).
The word superiority effect, first demonstrated by Cattell (1886) and later explored in
detail by Reicher (1969) and Wheeler (1970), provides additional evidence for the interactive
nature of word recognition. This effect suggests that letters within words are more easily
recognized than letters in isolation or in random strings, supporting the idea that orthographic
processing is influenced by lexical knowledge (Perfetti & Helder, 2022).

18

Phonological and Dual-Route Models of Reading
While orthographic processing is crucial for word recognition, phonological processing
also plays a key role, particularly in early reading acquisition (Grainger, 2022). Phonetic theories
are based on the premise that all word orthographies systematically represent phonology, making
phonological processing the primary mechanism of word recognition (Liberman et al., 1977;
Gough, 1972). These theories propose a bottom-up reading process, in which readers decode
written words by associating phonemes (speech sounds) with their corresponding graphemes
(letters or letter combinations). As such, phonological awareness, the ability to recognize and
manipulate spoken components of words, exists at multiple levels, including word, syllable,
onset and rime, and phoneme levels (Gillon, 2017). These phonological decoding theories are
primarily grounded in alphabetic languages and may not fully capture the reading processes
involved in logographic systems such as Chinese, where orthographic-to-phonological mappings
are less systematic; however, both systems rely on mapping visual word forms to meaning,
engaging universal cognitive processes that are shaped by language-specific constraints and
demands (Perfetti & Helder, 2022).
In contrast, whole-word theories emphasize that skilled readers recognize entire words as
meaningful units rather than deconstructing them into phonemes. Sometimes called the sightword approach, this perspective focuses on the memorization and visual recognition of words
rather than phoneme-grapheme correspondence (Gough, 1972; Gough & Hillinger, 1980). This
theory differs from phonetic theories, arguing that word recognition relies on stored lexical
representations rather than phonological decoding.
A more comprehensive explanation of word recognition is offered by the dual-route
model of reading, which suggests that written words can be processed through two parallel

19

pathways (Coltheart, 2006). The lexical route accesses the semantic lexicon directly, enabling
fluent word recognition without phonological mediation. In contrast, the phonetic (sublexical)
route deciphers words through a letter-to-sound conversion process (Perfetti & Helder, 2022).
This model accounts for differences in how regular (i.e., those that follow standard phonetic
rules and therefore can be easily decoded using common letter-sound correspondences) and
irregular words (i.e., those that do not follow common phonetic rules and often must be
memorized) are read, as well as how phonology influences silent reading.

Figures 1 and 2
Dual-Route Theories of Reading.

Note. Reprinted from “Dual Route and Connectionist Models of Reading: An Overview,” by M.
Coltheart, 2006, London Review of Education, 4(1), 5–17. Reprinted under fair dealing
provisions for the purpose of research and education.

However, recent research suggests that phonological activation occurs even in silent
reading, influencing both word identification and meaning retrieval (Seidenberg et al., 2022).
Perfetti and Helder (2022) argue that phonological representations are automatically activated

20

even in tasks that do not require pronunciation, as demonstrated by masked priming studies
showing early and involuntary phonological involvement during silent reading. Their Reading
Systems Framework highlights the rapid and interactive integration of orthographic,
phonological, and semantic processes during word recognition. Drawing on behavioural,
computational, and neuroimaging evidence, they show that phonological codes are accessed
almost immediately, before a word is fully identified, and contribute not only to decoding but
also to comprehension. They further note that while reliance on phonology may vary across
writing systems, phonological processing remains a universal component of skilled reading.
These findings challenge earlier assumptions that phonology plays only a post-lexical role in
reading and instead support a parallel activation model, where phonology, orthography, and
semantics interact dynamically during word recognition (Seidenberg et al., 2022).
Expanding upon the dual-route model, Grainger (2018) proposed that orthographic
processing serves as an interface between visual perception and linguistic processing in what he
terms a “mid-level vision of reading.” According to this view, visual features activate letter
representations, which then provide input to three pathways for meaning retrieval: (1)
orthography-to-semantics via whole-word representations, (2) orthography-to-morphosemantics
via morphological representations, and (3) orthography-to-phonology through sublexical
spelling-sound correspondences (Grainger, 2018). These pathways interact, demonstrating the
complexity of how phonology and orthography contribute to reading comprehension.

21

Figure 3
Dual-Route Model of Visual Word Recognition.

Print

Speech

Orthographic
Feature
Encoding

Phonetic
Feature
Encoding

Graphemic
Representations

Orthographic
Lexical
Representations

(PD)

(S V)

Phonemic
Representations

Phonological
Lexical
Representations

Semantic
Representations

Speech

Note. Reprinted from “Examining the Interactivity of Lexical Orthographic and Phonological
Processing,” by W. J. Owen & R. Borowsky, 2003, Canadian Journal of Experimental
Psychology / Revue canadienne de psychologie expérimentale, 57(4), 290. Reprinted under fair
dealing provisions for the purpose of research and education. PD = phonetic decoding (i.e.,
sublexical, assembled phonology) route, SV = sight vocabulary (i.e., lexical, addressed
phonology) route.

Within this framework, Owen and Borowsky (2003) conducted a series of experiments to
examine the relationship between orthographic and phonological processing in reading. They
found compelling evidence that orthographic processing plays a dominant role in activating
phonological representations — a stronger influence than previously assumed. Specifically, their
results indicated that visual word forms automatically activate corresponding phonological
representations, facilitating word recognition.

22

Their findings revealed an asymmetrical effect: seeing a word strongly influenced how
participants recognized its spoken form, but hearing a word had much less impact on recognizing
its written form. This asymmetry suggests that orthographic (visual) information has a greater
impact on phonological processing than phonological information has on orthographic
processing.
Owen and Borowsky (2003) further examined how phonology and orthography interact
during word recognition. They found both facilitatory and inhibitory effects by presenting threeletter word triplets (e.g., cap/map/rap) as orthographic stimuli simultaneously with spoken word
targets. When the visual word matched the spoken word (e.g., seeing “cap” while hearing “cap”),
word recognition was faster and more accurate. However, when the visual and spoken words
conflicted (e.g., seeing “cap” but hearing “rap”), participants experienced interference, leading to
slower and less accurate recognition.
Crucially, their results showed that orthographic processing (seeing a word) had a
stronger influence on phonological discrimination (recognizing the spoken word) than the
reverse. This supports models suggesting that reading involves dominant connections from
orthographic to phonological processing, rather than equally bidirectional pathways. These
findings challenge models assuming equal interaction between phonological and orthographic
processes and suggest that reading relies more heavily on learned mappings from print to sound
rather than the reverse.
Connectionist Models
Connectionist models, also known as parallel distributed processing models, offer a
fundamentally different framework than dual-route theories. Rather than relying on two distinct
processing routes, connectionist models emphasize a single, interactive system in which word

23

recognition emerges from patterns of activation across a distributed network (Seidenberg &
McClelland, 1989; Plaut et al., 1996). These models propose that orthographic, phonological,
and semantic information is processed simultaneously, with representations learned gradually
through repeated exposure and statistical regularities in the language.
Unlike dual-route models, connectionist frameworks reject the idea of modular
processing. Instead, they highlight probabilistic learning, through which readers become
sensitive to regularities in the mapping between orthography and phonology. Word recognition is
thus shaped by how frequently a word has been encountered, how consistent its spelling-tosound correspondences are, and how strongly it is connected to other representations in the
language system.
Computational models further support the probabilistic and interactive nature of word
recognition. The connectionist approach proposes that repeated exposure to words enables the
mapping of orthographic inputs onto phonological and semantic representations (Seidenberg et
al., 2022). This view is reinforced by earlier work from Seidenberg (1987), who demonstrated
that readers become sensitive to frequently occurring sublexical structures, such as letter clusters,
not because they act as symbolic access units, but because of their statistical regularity across the
lexicon.
Recent findings from Perfetti and Helder (2022) also align with the connectionist view.
Their Reading Systems Framework emphasizes the automatic and rapid activation of
phonological codes during silent reading. Drawing on behavioural and neuroimaging evidence,
they show that orthographic, phonological, and semantic processes become active in parallel,
even before full word identification is achieved. This challenges the notion that phonological

24

activation is purely post-lexical and reinforces the idea of dynamic, interactive processing during
reading.
Similarly, Seidenberg et al. (2022) assert that phonological activation is a universal
feature of skilled reading across writing systems, though the degree of reliance may vary. For
instance, while alphabetic languages like English involve strong phoneme-grapheme mappings,
logographic languages such as Chinese depend more on morpho-semantic information.
Nevertheless, phonology remains integral across orthographic systems.
Connectionist models have also been instrumental in explaining developmental reading
disorders, including dyslexia. Rather than attributing such difficulties to deficits in discrete
reading pathways, these models view them as disruptions in learning the statistical structure of
written language. This perspective offers a more flexible and individualized understanding of
reading development and is especially useful for modelling variability in literacy acquisition
across different orthographies and learner populations.
Cognitive and Neural Mechanisms in Word Recognition
Advancements in cognitive neuroscience have provided significant insights into how the
brain processes written language. Rather than following a strictly sequential path from visual
perception to comprehension, word recognition involves the parallel activation of orthographic,
phonological, and semantic representations, supported by specialized neural regions (Perfetti &
Helder, 2022).
One of the most critical brain regions for word recognition is the VWFA, located in the
left fusiform gyrus. This region is responsible for processing orthographic patterns and interacts
with other language-related areas to facilitate fluent reading (Grainger, 2022). Rather than simply

25

storing static word forms, the VWFA dynamically integrates visual information with
phonological and semantic cues, supporting efficient lexical access.
Eye-tracking and neuroimaging studies have demonstrated that word recognition is a
highly interactive process, with multiple linguistic representations activated simultaneously
rather than sequentially (Perfetti & Helder, 2022). For example, event-related potential (ERP)
studies indicate that phonological activation occurs within 200 milliseconds of word
presentation, even in silent reading, suggesting that orthographic and phonological processing
are tightly coupled (Seidenberg et al., 2022).
Additionally, beyond the VWFA, broader cortical networks, including the inferior frontal
gyrus (linked to phonological decoding) and the middle temporal gyrus (associated with meaning
retrieval), play key roles in reading (Price, 2012). These findings reinforce the idea that word
recognition is a distributed process, engaging multiple brain areas in a coordinated manner. This
view is further supported by neuroimaging evidence from Petersen et al. (1990), who used PET
imaging to demonstrate that medial extrastriate cortex is activated by both real words and
pseudowords, reflecting sensitivity to orthographic regularity, whereas only real words elicited
activation in the left inferior frontal cortex, a region associated with semantic processing.
These results suggest a functional dissociation between visual word form recognition and
semantic access, lending support to the distinction between early, automatic visual processing
and higher-order, meaning-based activation in the reading network. By considering both
neurobiological and computational perspectives, it becomes clear that word recognition is not
merely a visual task but a dynamic process that integrates multiple cognitive systems, allowing
for rapid and accurate word identification.

26

Effects of Recurrent Childhood Otitis Media on Reading in Young Adulthood
Ear infections (otitis media) can influence the typical development of language
acquisition and language processing mechanisms. According to Carroll and Breadmore (2018),
the chance that children will have at least one episode of otitis media is 83%, with 46% having
recurring otitis media episodes before age three.
Clinical subtypes of otitis media include acute otitis media, otitis media with effusion,
and recurrent otitis media (Aarhus et al., 2015). Acute otitis media is the first presentation of
otitis media that occurs and is associated with a brief onset and duration, fever, intense pain, and
increased pressure in the ear (Winskel, 2006). Acute otitis media affects almost 80% of all
children before age four or five (Stålnacke, 2022; Jamal et al., 2022).
Acute otitis media can lead to middle ear effusion, where fluid remains in the middle ear
after the infection subsides. This residual fluid can obstruct sound wave transmission, resulting
in mild to moderate hearing loss that may impair early auditory processing and language
development during infancy and toddlerhood (Winskel, 2006; Cai et al., 2018). Although otitis
media with effusion typically resolves within three months without intervention, children may
still experience a critical period of reduced auditory input during this time (Cai et al., 2018).
Recurrent otitis media occurs when there are multiple episodes of acute otitis media or
otitis media with effusion. Recurrent otitis media is defined as three or more episodes of acute
otitis media during a six-month time frame or four or more in a period of twelve months (Krakau
et al., 2017).
It is critical to note that otitis media has a peak incidence rate at 16 months, coinciding
with the critical period of learning language sounds known as perceptual narrowing, when
infants “tune in” to the sounds of their native language before the age of 16 months (Winskel,

27

2006). If otitis media with effusion occurs during the critical period of language development,
the infant may experience transient hearing loss and receive inconsistent auditory signals (Mody
et al., 1999). Fluctuating speech signals may result in difficulty drawing out consistencies from
the speech stream, making the acquisition of phonological awareness more difficult (Mody et al.,
1999). Difficulties distinguishing and acquiring phonemes (i.e., the smallest speech units) may
lead to phonological deficits (Carroll & Breadmore, 2018). Limited auditory input can lead to a
weak encoding of auditory-based phonological distinctions, and underspecified phonological
representations may also lead to insensitivity to phonetic characteristics of sounds, affecting the
ability to identify and discriminate between sounds (Mody et al., 1999).
Significance of Phonological Awareness
Phonetic categories are essential for recognizing phonemes across a variety of contexts.
The development of phonological awareness allows children to implicitly understand that words
are constructed from a limited number of sounds (Carroll & Breadmore, 2018). Phonological
representations are essential for identifying and discriminating sounds (Mody et al., 1999). The
formation of phonological representations is thought to occur between the ages of 12 months and
three years and further becomes classified into phonological categories (Carroll & Breadmore,
2018). Partial auditory input resulting from recurrent otitis media can disrupt the formation of
phonetic categories and phonological representations, causing them to be limited (Carroll &
Breadmore, 2018).
Phonological deficits in children with a history of recurrent otitis media may stem from
reduced auditory exposure in early childhood, which can disrupt the formation of stable
phonological representations (Mody et al., 1999). In a study using speech perception and verbal
short-term memory tasks, Mody et al. (1999) found that these children performed less accurately

28

than controls when identifying syllable pairs with subtle phonetic differences (e.g., ba vs. da).
They made significantly more errors when the sounds differed by only one phonetic feature
(such as voicing or place of articulation), suggesting difficulty distinguishing fine-grained
phonological contrasts. These contrasts form the basis of phonological categories — groupings
of similar sounds that support word recognition and language development (Berti et al., 2020).
Without consistent early auditory input, these categories may remain underdeveloped, leading to
long-term challenges in speech perception and processing. Supporting this, Zumach et al. (2010)
found that children who experienced more frequent and severe episodes of otitis media in early
life performed significantly worse on speech-in-noise recognition tasks at age 7. This suggests
that early auditory disruptions may have lasting effects on higher-order auditory processing,
which plays a critical role in distinguishing speech sounds in everyday environments.
Phonological awareness, the ability to consciously recognize and manipulate the sound
structures of language, such as syllables, onsets, rimes, and phonemes, is essential not only for
distinguishing words in auditory form but also for understanding the relationship between print
words and their corresponding sounds (Carroll & Breadmore, 2018). As noted earlier, reading is
a multimodal activity that engages orthographic, semantic, and phonological processes.
Phonological impairments may contribute to the development of reading difficulties.
Phonological encoding is an essential foundation for learning how to read because to decode
words, children must implicitly learn that words are made up of a limited number of similar
sounds across different words and are governed by a specific set of rules (Carroll & Breadmore,
2018). Thus, phonological awareness impacts not only listening to language but also reading.
Winskel (2006) assessed phonological awareness in children aged six to eight by
administering a non-word reading task — a standard measure that tests a child's ability to decode

29

unfamiliar letter strings. Children with a history of recurrent otitis media performed significantly
worse than their peers without such a history, suggesting difficulty in mapping phonemes to
graphemes. In addition to poorer phonological decoding, these children showed weaknesses in
expressive vocabulary and word definition tasks, indicating broader semantic deficits. However,
because the children were still in the early stages of reading acquisition, Winskel noted that some
impairments may not have been fully developed or detectable at the time of testing. This
possibility is supported by findings from Mody et al. (1999), who emphasized that phonological
impairments are often subtle and can go undetected by standardized assessments.
While reading typically involves decoding written words using phonological rules, the
complexity of this process becomes apparent when considering irregular words — those that do
not follow predictable spelling-to-sound correspondences. For example, the word “pint” cannot
be reliably decoded using standard phonological strategies and must instead be memorized as a
whole. This highlights the importance of phonological awareness, the ability to identify and
manipulate the sound structures of language, for both decoding regular words and recognizing
irregular exceptions. However, research suggests that children with a history of recurrent otitis
media may struggle to develop stable phonological categories due to inconsistent early auditory
input, making it more difficult to distinguish similar-sounding words and increasing their
reliance on rote memorization for irregular items (Carroll & Breadmore, 2018).
To examine the relationship between auditory experience and reading-related skills,
Carroll and Breadmore (2018) compared two groups of school-aged children: those with a
history of recurrent otitis media and those identified as poor readers. While both groups
demonstrated phonological weaknesses, the otitis media group showed a distinct pattern of
impairments, particularly in tasks requiring phoneme segmentation and blending. These findings

30

suggest that although both groups experienced reading difficulties, the underlying causes
differed. Children with recurrent otitis media appeared to have phonological deficits linked
specifically to reduced early auditory input, distinguishing their profile from that of age-matched
poor readers with broader language impairments.
Complementing this focus on phonological processing, Cai and McPherson (2016)
conducted a systematic review to investigate how auditory disruptions associated with otitis
media influence children's ability to perceive speech, a foundational skill for developing accurate
phonological representations. Their findings revealed that children with otitis media with
effusion often experience mild to moderate conductive hearing loss, which negatively affects
speech perception in both quiet and noisy environments. Despite variability in assessment
methods, the review emphasized that many children are underdiagnosed and untreated, leaving
them vulnerable to developmental delays. Extending these findings, Cai et al. (2018) compared
speech perception in children with actual otitis media-related hearing loss, simulated hearing
loss, and normal hearing. Results showed that while both real and simulated hearing loss
impaired speech understanding in quiet, children with actual hearing loss performed significantly
better in noisy conditions than those with simulated hearing loss, suggesting that real-world
auditory experience may lead to partial compensatory strategies. Together, these findings
demonstrate that even mild hearing loss from otitis media can have immediate and measurable
effects on speech perception, which in turn may disrupt the acquisition of phonological
representations critical for reading.
Importantly, these early disruptions appear to have long-term consequences. Delage and
Tuller (2007) found that adolescents with mild-to-moderate hearing loss do not follow typical
language development trajectories. Their study of poor readers identified distinct profiles

31

between adolescents with a history of hearing loss and those with specific language impairments.
While both groups showed language difficulties, adolescents with hearing loss exhibited
persistent deficits in morphosyntactic skills and sentence repetition — deficits that had not
resolved with age. These findings provide compelling evidence that the effects of early auditory
deprivation, such as that caused by recurrent otitis media, can extend well beyond the early years
and may persist into adolescence and beyond.
Collectively, these studies underscore a critical point: early auditory input plays a
foundational role in the development of phonological awareness, speech perception, and higherlevel language abilities. The evidence suggests that even temporary or mild hearing disruptions
during sensitive developmental windows can have lasting effects. This supports the central
argument of the present research that phonological development in individuals with a history of
otitis media may follow an atypical trajectory, with implications for reading and language
outcomes well into later childhood and adolescence.
Long-lasting Effects of Otitis Media
Recurrent otitis media is suspected to have long-lasting impacts on speech and language
skills, including language delays, phonological impairments and reading difficulties. One large
cohort study indicated that acute recurrent otitis media in childhood is associated with adult
hearing loss (Aarhus et al., 2015). However, not all research findings have been conclusive
regarding the long-lasting effects of childhood recurrent otitis media: despite several studies
revealing long-lasting phonological impairments resulting from childhood recurrent otitis media
(Carroll & Breadmore, 2018; Mody et al., 1999). Zumach et al. (2010) conducted a longitudinal
study following 65 Dutch children from infancy to school age and found no significant long-term
differences in language comprehension or production between children who experienced early-

32

life otitis media and those who did not. The authors concluded that while otitis media and related
hearing loss may temporarily affect early language development, these effects resolve over time,
with children achieving age-appropriate language outcomes by ages 6 to 10. This finding
contrasts with earlier studies and suggests that the long-term impact of otitis media on language
skills may vary depending on factors such as the duration and severity of hearing loss,
intervention timing, and individual resilience.
Investigating the effects of recurrent otitis media in early childhood on phonological
processing in adulthood is crucial for understanding its long-term impact on language perception.
Examining how early auditory disruptions influence phonological skills over time can provide
valuable insights into the cognitive mechanisms underlying language comprehension and
production in adults. This research can also inform educational and clinical approaches aimed at
supporting individuals who may experience subtle but lasting effects on their phonological
processing abilities.
Experiment
Method
Participants. Research participants aged 18 to 30 were recruited via the UNBC
Department of Psychology’s research participation system in return for course credit. Before
taking part, the participants were requested to confirm their exact history of childhood ear
infections with their caregivers. Based on their self-reported history of otitis media, participants
were categorized into (1) a group with childhood recurrent otitis media if they experienced three
or more recurring episodes of ear infections in childhood (N = 22) and (2) a group that never had
otitis media in childhood (N = 36). Participants who reported one or two instances and those who
were unsure about their history of childhood otitis media were excluded from the analysis (N =

33

26). Both groups included male and female participants who had normal (or corrected to normal)
vision, were native speakers of English, and had no learning or reading disabilities, comprising a
total of 58 research participants.
Materials. This section describes the experimental design, stimuli, software, and
procedures used to examine phonological processing in participants with and without a history of
recurrent otitis media.
Design and Stimuli. The two-alternative forced-choice paradigm developed by Owen
and Borowsky (2003) served as the foundation for the present study’s design to investigate
facilitation and inhibition effects in young adults and determine whether individuals with a
history of recurrent otitis media exhibit impaired phonological awareness, defined as the ability
to recognize and manipulate the sound structure of words. This study used a mixed factorial
design, with one within-subjects factor (context condition: congruent, incongruent, irrelevant)
and one between-subjects factor (group: recurrent otitis media history vs. control). The primary
independent variables were context condition and group, and the dependent variables were
response accuracy and reaction time.
The hypothesis predicted that participants without a history of early recurrent otitis media
would demonstrate a facilitation pattern, where congruent orthographic and phonological
information enhances accuracy more than incongruent information hinders it, a pattern consistent
with the results reported in Owen and Borowsky’s (2003) original study. They found that
phonological congruency effects in word recognition tasks typically follow a quadratic trend,
with the highest accuracy in congruent trials, moderate performance in irrelevant trials, and the
lowest accuracy in incongruent trials. This graphical pattern provides a diagnostic indicator of
facilitation versus interference effects in phonological processing. In contrast, participants with a

34

history of early recurrent otitis media were expected to show comparable levels of facilitation
and inhibition, suggesting that disrupted early auditory input may have resulted in weaker or
incomplete phonological representations — the mental sound-based codes — that enable
phonological awareness.
To evaluate these hypotheses, the analysis used a mixed factorial ANOVA to compare
mean accuracy across the three context conditions (within-subjects) and between the two groups
(between-subjects). If supported, such findings would provide evidence that recurrent otitis
media can produce subtle but lasting effects on phonological processing and broader language
development.
Software and Presentation. Owen and Borowsky (2003) added low-level white noise to
spoken word targets to degrade auditory stimuli, increasing task difficulty and sensitivity to
context effects. The present study adopted this approach using noise-masked auditory stimuli to
evaluate phonological processing under challenging listening conditions. Clinical speech-innoise tests often incorporate background conversations or multi-talker babble (Brady & Calcus,
2021), but such stimuli can introduce semantic interference known as the “cocktail party effect.”
To minimize this confound, the study employed white noise instead of spoken distractors,
ensuring that signal degradation occurred without introducing linguistic content that might
redirect attention. The experiment was designed and administered using the Gorilla Experiment
Builder (gorilla.sc), and all participants conducted the sessions on a desktop computer in the
psychology lab at the University of Northern British Columbia (UNBC).
To measure facilitation and inhibition effects, the experiment presented an orthographic
“context” word in the visual modality simultaneously with a degraded target word in the auditory
modality. Context-target pairings fell into one of three conditions: congruent (e.g., context:

35

“cap”; target: /cæp/; response options: “cap”/“map”), incongruent (e.g., context: “cap”; target:
/mæp/; response options: “cap”/“map”), or irrelevant (e.g., context: “cap”; target: /mæp/;
response options: “map”/“rap”). The stimulus degradation was performed in Audacity by
applying white noise to the signal level measured at 60 dB.
Procedure. The participants were greeted by a research assistant at the lab and seated in
front of the computer with the experiment start screen. The research assistant was seated within
one meter of the participant to guide them through the instructions and answer any questions
they had. Before beginning the experiment, the participants provided consent, with an option to
withdraw from the experiment at any time by closing the web browser. Data on basic
demographics (gender, age, first and second languages) were collected. The whole experiment
took approximately 20 minutes to complete1.
Each trial followed a consistent sequence: (1) a fixation mark (“&&”) appeared at the
center of the screen; (2) participants pressed the space bar to initiate the trial; (3) following a
100-ms interstimulus interval, a clearly visible orthographic stimulus (i.e., either “cap”, “map”,
or “rap”) appeared in the centre of the screen simultaneously with the degraded auditory target
(i.e., either /cæp/, /mæp/, or /ræp/), both presented for 500 ms; and (4) after another 100-ms
interstimulus interval, a two-alternative forced-choice response probe appeared in bright white
text against a black background, two lines below the original context word (e.g., heard “cap”
[press left arrow]; heard “rap” [press right arrow]). The software controlled all timing parameters
and recorded both response accuracy and reaction time. Participants completed a total of 36

1

Several conditions differed in the replication study as compared to the original experiment, including the
lighting in the lab, the speakers, the hardware, the software, and the decibel level of the white noise used in the
stimuli.

36

practice trials followed by 504 experimental trials, with 168 trials presented in each of the three
conditions (congruent, incongruent, and irrelevant).
Ethics Approval
Research ethics approval was obtained from UNBC (approval number
E2022.0107.001.00 – Appendix 1) to ensure adherence to the Tri-Council Policy Statement:
Ethical Conduct for Research Involving Humans as outlined by the Panel on Research Ethics
(2023). All responses were anonymized to protect participant confidentiality, and data were
stored securely on password-protected institutional servers. The consent form included a separate
section allowing participants to opt out of having their data included in the final analysis. No
identifying information was linked to individual responses, ensuring that all data remained
confidential throughout the study.
Data Analysis
A Mixed Factorial analysis of variance (ANOVA) was conducted to analyze the results,
with groups (participants with or without a childhood history of otitis media) as the betweensubjects factor and congruency (congruent, irrelevant, or incongruent) as the within-subjects
factor. Data preprocessing and visualization were performed using the R programming language
in R (version 4.3.1 GUI 1.79 Big Sur ARM build (8238), R Core Team, 2023) and R Studio
(version 2023.06.1+524 (2023.06.1+524), 2023) and its associated packages. Data analysis was
performed in Jamovi (version 2.3.21.0).
Results
Accuracy scores, defined as the percentage of correct responses out of the total number of
trials, served as the primary dependent variable. Table 1 displays mean accuracy scores and

37

standard deviations for all three congruency conditions — congruent, irrelevant, and incongruent
— across the two participant groups.

Table 1.
Experiment Demographics
M(SD)

M(SD)

M(SD)

OM in childhood

Congruent

Irrelevant

Incongruent

Never (N = 36)

0.787(0.124)

0.654(0.131)

0.537(0.183)

OM Group (N = 22)

0.761(0.0767)

0.661(0.104

0.574(0.111)

Note. Descriptive statistics. The “Never” group consists of participants who have never had ear
infections; the “OM” group consists of participants with three or more instances of ear infections
in childhood.

A mixed-factor ANOVA for congruency and the presence of otitis media in childhood
revealed a significant main effect for congruency, F(2, 112) = 83.12, MSE = 0.01, p < .001,
partial η² = .597, indicating a large effect. There was no significant two-way interaction, F(2,
112) = 1.75, MSE = 0.01, p = .18. The between-subjects factor, the presence of otitis media in
childhood, was not significant, F(1, 56) = 0.0373, MSE = 0.04, p = .85.
Post hoc analysis using the Tukey method revealed significant differences among all
three conditions, with all p-values < .001. The mean differences were as follows: congruent vs.
irrelevant condition of 0.117, congruent vs. incongruent of 0.218, and irrelevant vs. incongruent
of 0.102.
Therefore, there was no significant difference in the accuracy of responses between
individuals with or without a history of otitis media infections in childhood. The congruency
38

affects the accuracy of all participants’ responses, but there are no apparent residual effects of
childhood otitis media infections in young adults. The lack of significance for the betweensubjects factor suggests that any potential differences in the accuracy of responses are unrelated
to the history of otitis media in childhood.
These results indicate no significant difference in phonological processing between the
two groups. Consistent with Zumach et al. (2010), the findings do not provide evidence that
phonological deficits arising from recurrent otitis media persist into adulthood.

Figure 4.
Estimated Marginal Means

Note. Condition ✻ otitis media (OM) in childhood group.

Discussion
The results of this study reinforce the importance of phonological congruency in word
recognition. The significant main effect observed for congruency suggests that phonological
activation is integral to visual word recognition, a finding compatible with core principles from
39

both dual-route and connectionist models of reading (Coltheart, 2006; Seidenberg et al., 2022).
These results align with prior research indicating that phonological information is activated
automatically, even in silent reading (Perfetti & Helder, 2022). However, the findings do not
provide strong evidence for the lingering effects of childhood otitis media on phonological
processing in young adulthood. These findings suggest that either compensatory mechanisms
have effectively mitigated the impact of early auditory disruptions or that the current sample size
lacked the statistical power to detect more subtle, lingering effects.
Stanovich’s (1980) interactive-compensatory model provides a compelling theoretical
lens to interpret this possibility. This model posits that reading is not governed by a rigid
hierarchy of processes, but rather by a flexible and interactive system in which weaknesses in
one domain (e.g., phonological processing) can be offset by strengths in others (e.g.,
orthographic or semantic processing). Accordingly, individuals with early phonological deficits,
such as those resulting from recurrent otitis media, may still attain reading fluency by relying
more heavily on higher-level contextual or visual cues.
In a related framework, Stanovich’s (2009) Matthew Effect framework highlights the
cumulative and self-reinforcing nature of reading development. Early proficiency in
phonological processing can lead to greater print exposure, vocabulary acquisition, and general
cognitive growth. In contrast, early difficulties may initiate a downward spiral characterized by
reduced reading engagement and slower linguistic development. Nevertheless, the absence of
significant long-term deficits in this study may reflect successful compensation or access to
enriched learning environments that helped interrupt this negative trajectory. Together, these
models underscore the importance of both foundational skills and ongoing reading experience in
shaping literacy outcomes across development.

40

While previous studies, such as Owen and Borowsky (2003), have demonstrated a
quadratic function in the graphical depiction of their results, our replication did not yield the
same pattern. In the present study, we observed a linear function. The differences in the pattern
of results could indicate a change in cognitive mapping for facilitation dominance to bias (i.e.,
equal facilitation and inhibition) or differences in methodological and contextual factors, such as
differences in participant demographics, stimulus presentation, and environmental conditions
that may have influenced word processing outcomes. For instance, the original study was
conducted in a dimly lit laboratory, whereas our study took place in a well-lit environment,
which may have subtly affected participants’ visual processing or attentional focus. Additionally,
procedural differences, such as software updates or display characteristics, could have played a
role in shaping response patterns.
Despite these variations, the results align with previous research on phonological
processing, showing that congruency plays a significant role in word recognition accuracy. The
study further supports the view that phonological activation occurs even in silent reading, which
challenges traditional assumptions about post-lexical phonological processing (Seidenberg et al.,
2022). Additionally, it underscores the broader significance of phonological awareness in both
early and skilled reading, reinforcing its role in cognitive models of literacy.
These findings also align with the notion that phonological processing is not isolated but
interwoven with other reading components. The interactive-compensatory model further supports
the idea that readers flexibly engage available cognitive resources depending on their individual
strengths and weaknesses. This view accommodates the developmental plasticity that is
suggested by the findings of this study.

41

Furthermore, practical applications of these findings suggest that individual expertise,
such as stenographers’ ability to engage in phonetic transcription, may alter the functional
relationship between phonological and orthographic processing. Fowler and Dekle (1991)
provided evidence that both visual and tactile modalities can influence speech perception,
suggesting that multiple sensory inputs may contribute to phonological processing. Although
their study focused primarily on speech perception, it raises questions about whether similar
cross-modal interactions exist in written word recognition, particularly for individuals with
specialized training.
Limitations of the Study
While the study provides meaningful insights, certain limitations must be acknowledged.
First, the sample consisted exclusively of young adults, limiting generalizability to other age
groups. Since phonological processing evolves across the lifespan, future research should
examine whether similar patterns are observed in children and older adults.
Second, the reliance on self-reported histories of otitis media introduces potential recall
bias. Future studies could incorporate audiological assessments or medical records to classify
participants based on early auditory health history more accurately.
Third, environmental factors such as laboratory conditions and stimulus presentation
methods may have influenced participant responses. Although these variables were controlled to
the extent possible, future research should explore how subtle differences in testing
environments impact phonological processing outcomes.
Future Directions
Several promising avenues for future research emerge from this study. Future research
should aim to address the limitations outlined above by incorporating more controlled

42

experimental conditions and objective measures of otitis media history. Longitudinal studies
could track individuals from early childhood into adulthood to better understand how
phonological processing develops over time. Such research would clarify whether compensatory
mechanisms emerge gradually and how they interact with literacy development.
Neuroimaging studies may also offer valuable contributions by identifying the neural
correlates of phonological and orthographic processing in individuals with a history of otitis
media. Researchers can explore how early auditory disruptions impact brain activation patterns
during reading tasks by utilizing techniques such as functional MRI and ERPs.
Additionally, further investigation into specialized populations, such as stenographers
and interpreters, could provide a deeper understanding of how phoneme-to-grapheme mapping
expertise influences cognitive processing. Research in this area could help refine models of word
recognition and contribute to educational strategies for training individuals in phoneme-based
transcription.
Conclusions
This chapter has explored the cognitive mechanisms involved in single-word processing,
highlighting the interaction between orthographic and phonological systems. The dual-route and
connectionist models offer valuable theoretical perspectives on how readers recognize words and
extract meaning from text, highlighting both rule-based decoding and distributed, interactive
processing.
While prior research has raised concerns about the potential long-term effects of early
auditory disruptions, especially recurrent otitis media, on phonological awareness and reading
development, the present study did not find evidence of enduring deficits in young adulthood.
Accuracy in phonological discrimination tasks did not significantly differ between participants

43

with and without a childhood history of otitis media. These findings suggest that early
phonological impairments, if present, may have resolved over time or been mitigated through
compensatory processes.
The significant main effect of phonological congruency reinforces its essential role in
visual word recognition, indicating that readers benefit from consistent orthographic and
phonological cues. However, the absence of group differences implies that the impact of early
auditory deficits may diminish with age, potentially due to the brain’s cognitive plasticity or
external supports such as early intervention and enriched learning environments.
These results appear inconsistent with Stanovich’s (2009) Matthew Effect framework,
which posits that early disadvantages in phonological processing can trigger a cumulative cycle
of reduced reading exposure and weaker literacy development. Instead, the findings suggest that
some individuals may have avoided this downward trajectory, possibly due to unmeasured
protective factors. Although this study did not directly evaluate those influences, the pattern of
results is consistent with successful remediation or resilience.
Conversely, the findings align with Stanovich’s (1980) interactive-compensatory model,
which emphasizes the flexible coordination of orthographic, phonological, and semantic systems
in reading. According to this model, when one domain is underdeveloped, other components of
the reading system can adaptively compensate. This compensatory mechanism may help explain
the observed reading proficiency despite early auditory disruption and underscores the variability
in reading development across individuals.
Overall, these findings underscore the importance of developmental timing,
environmental context, and cognitive flexibility in shaping long-term reading outcomes. A
deeper understanding of these factors is essential for designing effective early interventions and

44

educational practices that support resilience in individuals with a history of auditory or
phonological challenges.
Looking beyond the mechanisms of word recognition, the next chapter broadens the
focus from language processing to cognitive strategies that support effective learning more
generally. While this chapter emphasized how foundational skills like phonological and
orthographic processing contribute to reading, Chapter 3 explores how learners can actively
shape their educational outcomes through deliberate practice techniques. Drawing on insights
from cognitive psychology and educational research, it examines evidence-based strategies, such
as active recall, spaced repetition, and elaborative encoding, that enhance retention and support
long-term academic success. This transition marks a shift from understanding the cognitive
underpinnings of reading to applying cognitive principles to optimize how we learn.

45

3. Optimizing Postsecondary Learning Outcomes with Flashcard-Based Strategies
The importance of effective study strategies in higher education cannot be overstated,
particularly as students contend with growing academic demands and constrained preparation
time. Within this context, cognitive psychology offers a strong theoretical basis for
understanding how learning can be optimized through evidence-based strategies such as active
recall, spaced practice, and elaborative encoding (Dunlosky et al., 2013; Roediger & Butler,
2011). These strategies draw on key cognitive principles, including memory consolidation,
retrieval-induced reconsolidation, and the spacing effect, all of which support durable,
transferable learning (Carpenter et al., 2022; Roediger & Karpicke, 2006; Cepeda et al., 2006).
Flashcards represent a uniquely effective medium for operationalizing these principles
across both traditional and digital modalities. While conventional flashcards often emphasize
rote memorization, structured approaches such as the Flashcards-Plus strategy promote deeper
cognitive engagement through paraphrasing and the generation of personal examples (Appleby,
2013; Senzaki et al., 2017). While conventional flashcards often emphasize rote memorization,
structured approaches such as the Flashcards-Plus strategy aim to deepen cognitive processing by
incorporating elaborative encoding techniques. This method encourages students not only to
review definitions but to actively paraphrase the content in their own words and generate
personally meaningful examples (Appleby, 2013; Senzaki et al., 2017). These elaborative
prompts are designed to link new information to existing knowledge, which enhances
comprehension and supports long-term retention. Originally developed for use in university-level
psychology courses, the Flashcards-Plus method has been associated with improved
metacognitive monitoring and deeper conceptual understanding in learners (Appleby, 2013).
This method exemplifies elaborative encoding and aligns with the Levels of Processing
46

framework (Craik & Tulving, 1975), which suggests that memory retention improves when
information is processed at a deeper, semantic level rather than through shallow, surface-level
features such as appearance or sound.
Although the Levels of Processing framework has been influential in cognitive
psychology, it is not without criticism. Some scholars have noted that the theory suffers from
conceptual circularity — defining “depth” by its effects on memory performance — and lacks
clear operational definitions that can be consistently applied across studies or contexts (Lockhart,
2002; Baddeley, 2007). To address these limitations, the present research operationalizes
elaborative encoding using clearly defined instructional prompts, such as paraphrasing
definitions and generating personalized examples. These prompts move beyond abstract notions
of “depth” by providing concrete, measurable study behaviours that can be consistently
evaluated across learners. In doing so, Flashcards-Plus draws on the foundational insights of the
Levels of Processing framework while addressing its theoretical ambiguities through a
structured, empirically grounded design.
The study described here evaluates these strategies in both digital and traditional formats,
offering actionable insights into improving student engagement, retention, and academic success
across diverse educational contexts. A particular focus is placed on how flashcard-based
techniques function in real-world settings, especially as students increasingly rely on digital
platforms like Quizlet, Cram, and Anki. Quizlet is a widely used web-based study tool that
allows users to create and study custom flashcard sets using various modes such as Flashcards,
Learn, and Test formats (Quizlet, n.d.-a; Quizlet, n.d.-b). Cram also offers user-generated
flashcards and supports spaced repetition through the Leitner system, which adjusts review
frequency based on user performance (Cram.com, n.d.-b). Anki is an advanced flashcard

47

application that emphasizes long-term retention through algorithmic spaced repetition, allowing
learners to prioritize and customize study schedules (AnkiWeb, n.d.). The comparative benefits
of retrieval practice and spaced repetition — two well-established cognitive strategies — are
examined to determine their relative impact on long-term memory and performance.
Additionally, the Flashcards-Plus strategy is assessed across multiple interventions to
evaluate how structured elaboration and personalization support comprehension, application, and
conceptual understanding (Appleby, 2013). By analyzing both paper-based and digital
applications, this study provides a nuanced exploration of how learners can optimize study habits
through structured, evidence-based strategies.
This research also explores the role of self-regulated learning and learner autonomy in
implementing these strategies effectively (Zimmerman, 2002; Deci & Ryan, 1985). According to
research, active recall and spaced repetition require consistent engagement and goal-directed
behaviour — skills that are central to self-regulated and independent learning (Karpicke & Aue,
2015; Carpenter et al., 2022). Incorporating Flashcards-Plus may support metacognitive
monitoring by encouraging learners to reflect on their understanding and personalize their
encoding processes, a possibility supported by prior research demonstrating that elaborative
encoding strategies enhance metacognitive accuracy (Appleby, 2013; Senzaki et al., 2017).
Key theoretical constructs, such as judgements of learning (JOLs) and cognitive
engagement, are woven into the study as foundational frameworks. Research shows that JOLs
play a critical role in guiding study behaviour, yet students often misjudge the effectiveness of
strategies like retrieval and spacing (Koriat, 1997; Nelson & Dunlosky, 1991). This work also
addresses test anxiety, examining how structured study tools can help reduce stress and increase
confidence through predictable, mastery-oriented preparation methods (Rana et al., 2020).

48

The findings presented in this chapter are drawn from three experiments that collectively
investigate how retrieval practice, spaced repetition, and elaborative encoding impact retention,
metacognitive accuracy, and academic performance. These experiments compare different
flashcard-based learning strategies across modality and structure, providing empirical data on
what works, for whom, and under what conditions.
Together, the three experiments offer a nuanced and theoretically grounded
understanding of how flashcard-based learning can be adapted to enhance academic outcomes in
postsecondary education. This multi-faceted approach bridges cognitive psychology and
educational practice, ensuring that findings are both practically applicable and theoretically
informative. By integrating core strategies — retrieval practice, spaced repetition, and
elaborative encoding — structured flashcard use has the potential to foster deeper learning,
support metacognitive development, and improve test performance (Dunlosky et al., 2013;
Brown-Kramer, 2022).
Ultimately, flashcards serve as a powerful self-testing tool, capitalizing on core cognitive
mechanisms to support learning. By reinforcing recall through repeated retrieval, spacing
reviews to enhance memory consolidation, and encouraging elaboration through personalization,
these techniques offer students both efficiency and depth in their learning, whether delivered via
digital platforms or traditional materials (Roediger & Butler, 2011; McDaniel, 2023).
Retrieval Practice, Spaced Practice, and Their Digital Applications
Retrieval practice and spaced practice are two foundational learning techniques in
cognitive psychology, both of which have been consistently shown to improve long-term
retention and knowledge transfer. A central element of retrieval practice is active recall, the
deliberate retrieval of information from memory without external cues, a process that strengthens

49

memory traces and makes information more durable and accessible over time (Roediger &
Butler, 2011).
At the cognitive level, retrieval practice reinforces retrieval pathways, making recall
more automatic and resistant to forgetting (Carpenter et al., 2022). Neuroscientific evidence
suggests that repeated retrieval engages memory reconsolidation processes, strengthening
memory networks through hippocampal and dopaminergic interactions (Roediger & Butler,
2011).
The spacing effect, first documented by Ebbinghaus in 1885 (as cited from Ebbinghaus,
2013), demonstrates that distributing learning sessions over time significantly improves
retention. “Spaced practice” refers to the general distribution of study sessions over time,
whereas “spaced repetition” typically involves algorithm-driven reviews, such as those employed
in platforms like Anki (Carpenter et al., 2022; AnkiWeb, n.d.). Spaced repetition optimizes
memory consolidation by timing reviews just before forgetting occurs (Cepeda et al., 2006). This
approach reduces forgetting while strengthening memory, improving knowledge application
across domains. Research has found that spacing benefits learners across various educational
levels, from early childhood education to professional training programs (Carpenter et al., 2022).
Despite their shared effectiveness, retrieval and spacing strategies are underused by
students, who often rely on passive techniques such as rereading and highlighting — methods
that foster a false sense of fluency rather than lasting learning (Brown-Kramer, 2022). Karpicke
et al. (2009) suggest that this tendency may be driven by overconfidence in these passive
methods. To help close this gap, digital platforms like Quizlet and Cram offer scalable solutions
for implementing evidence-based study techniques.

50

Although both Quizlet and Cram support flashcard-based retrieval practice, the platforms
differ in how they prioritize cognitive strategies. Quizlet emphasizes retrieval through active
recall features like flashcards, tests, and matching games, while Cram focuses on spaced
repetition through the Leitner system, which schedules reviews based on recall performance.
Therefore, the comparison presented in Experiment 1 reflects differences in algorithmic design
and user experience, not mutually exclusive learning methods. However, it is important to note
that both platforms incorporate elements of both retrieval and spacing, and the results should be
interpreted in light of this overlap.
Quizlet is a versatile and widely adopted platform designed to facilitate vocabulary
acquisition and conceptual learning through a range of interactive study modes. The platform
supports retrieval-based learning via its “Flashcards” mode, which prompts users to actively
recall definitions, paired concepts, or categorical relationships (Özdemir & Seçkin, 2024).
Additionally, it incorporates adaptive learning sequences, meaning the software adjusts to user
performance and provides increased exposure to terms that require further reinforcement
(Quizlet, n.d.-b).
Beyond flashcards, Quizlet integrates multiple study modes, including “learn mode” that
uses adaptive sequencing to present material in spaced intervals, reinforcing long-term retention,
“test mode” that allows users to self-assess knowledge using automatically generated quizzes
with multiple-choice, true/false, and written-response questions, and “match mode” that
encourages rapid association between terms and definitions through gamified matching tasks.
In the present study, Quizlet flashcards were customized to align with experimental
learning conditions. Each flashcard consisted of a key concept on one side and its definition on
the other. For instance, a sample flashcard used in this study displayed the term “abstract

51

thinking” on one side and its definition, “capacity to understand theoretical concepts”, on the flip
side. Engaging with these materials across multiple study sessions encouraged students to
leverage retrieval-based learning and spaced reinforcement to optimize knowledge retention.
Cram, in contrast, specializes in spaced repetition through its implementation of the
Leitner system. This widely used algorithm organizes flashcards into increasingly larger intervals
based on user recall accuracy and study time to weaker material, thereby optimizing memory
consolidation (Cram, n.d.-b). This method systematically sorts flashcards into levels, ensuring
that frequently recalled material is reviewed less often, while incorrectly recalled items remain in
lower tiers for more frequent review (Serfaty & Serrano, 2020). The Leitner system has been
empirically validated as an effective spaced learning approach, particularly in second language
acquisition and factual recall learning (Serfaty & Serrano, 2020).
Unlike traditional study methods that rely on massed repetition, Cram’s platform
optimizes spaced learning by increasing the intervals between successful recall attempts (Cram,
n.d.-a). This approach ensures that learners spend more time reviewing difficult concepts while
reinforcing stronger knowledge at optimal intervals, reducing cognitive overload and enhancing
long-term retention (Yuan, 2022). Additionally, its “Memorize” mode follows an adaptive
review schedule, gradually increasing the time between exposures for correctly answered items
to prevent premature forgetting (Serfaty & Serrano, 2020).
From a cognitive perspective, retrieval practice and spaced practice represent two distinct
yet complementary mechanisms for enhancing long-term memory. Retrieval practice strengthens
memory through active recall, reinforcing neural pathways associated with learning. In contrast,
spaced practice reduces forgetting by distributing learning over time, thereby facilitating memory
consolidation. Neuroscientific evidence supports both mechanisms, showing that each engages

52

critical brain regions such as the hippocampus and prefrontal cortex — areas implicated in
memory formation, retrieval, and cognitive control (Roediger & Butler, 2011; Yuan, 2022).
Despite their shared theoretical foundations, the relative benefits of retrieval versus
spacing remain underexplored in digital contexts, where learners rely on platforms like Quizlet
and Cram. These tools differ in how they emphasize each strategy: Quizlet primarily promotes
retrieval through active recall features like flashcards, tests, and matching games, whereas Cram
emphasizes spacing via the Leitner system, an algorithm that schedules review intervals based on
user performance. Such design differences offer a unique opportunity to examine how each
cognitive strategy functions when embedded in real-world learning tools.
The effectiveness of these approaches may depend on user engagement, interface
usability, and individual differences in motivation and self-regulation (Zimmerman, 2002; Deci
& Ryan, 1985). Accordingly, the present experiment evaluates the learning outcomes associated
with each platform, seeking to isolate the relative contributions of spaced repetition and retrieval
practice as distinct cognitive strategies. By comparing these approaches in a controlled setting,
this study aims to clarify their differential impact on retention and provide evidence-based
recommendations for optimizing digital study interventions.
Elaborative Encoding and the Flashcards-Plus Strategy
Elaborative encoding involves integrating new information with prior knowledge by
creating meaningful associations, such as paraphrasing or generating examples (Craik &
Tulving, 1975). The Flashcards-Plus strategy exemplifies this approach by requiring students to
personalize definitions and generate examples, promoting deeper cognitive engagement and
aligning with self-regulated learning theories (Zimmerman, 2002). By fostering active learning,

53

this method can enhance comprehension, retention, and application of information (Dunlosky et
al., 2013).
The effectiveness of elaborative encoding is further explained by the Encoding
Specificity Principle (Tulving & Thomson, 1973), which states that retrieval success is
maximized when encoding and retrieval cues align. Research supports that elaborative encoding
enhances retrieval practice by strengthening memory recall through the generation of multiple
retrieval pathways (McDaniel, 2023). Additionally, deep semantic processing leads to greater
memory durability compared to shallow processing (Craik, 2002), reinforcing the Levels of
Processing framework. This principle suggests that learning is most effective when information
is meaningfully structured and actively reconstructed.
The Flashcards-Plus strategy integrates both retrieval practice and spaced repetition to
maximize long-term retention. Unlike rote memorization, it encourages students to engage with
material at increasing intervals (progressively more extended periods between review sessions),
reinforcing memory consolidation and retrieval strength (Roediger & Butler, 2011). By
incorporating verbatim definitions (retention), paraphrased explanations (comprehension), and
personal examples (application), Flashcards-Plus fosters deeper encoding and conceptual
understanding (Appleby, 2013; Senzaki et al., 2017). Furthermore, it helps students navigate
different types of multiple-choice questions by strengthening self-explanation and problemsolving skills.
The efficacy of this method is supported by studies showing that elaborative encoding
combined with retrieval practice enhances retention beyond either strategy alone (McDaniel,
2023). By implementing strategies that promote meaningful learning connections, students can
improve both memory retention and conceptual understanding. As research suggests, combining

54

elaborative encoding, retrieval practice, and structured self-regulated learning tools leads to
higher academic performance and improved metacognitive accuracy, making Flashcards-Plus a
powerful and versatile learning strategy.
Modality Effects
The format in which flashcards are delivered may influence learning efficacy, as different
modalities engage cognitive processes in unique ways (Yuan, 2022). Traditional paper flashcards
provide a unique tactile experience, unlike digital-based flashcards. In this regard, embodied
cognition frameworks suggest that the physical act of handling and flipping paper flashcards
engages sensorimotor pathways, reinforcing learning through active interaction with the material
(Wilson, 2002). This process may strengthen memory retention by offloading cognitive work
onto the environment, reducing cognitive load, and fostering deeper conceptual understanding.
In contrast, digital flashcards leverage gamification, progress tracking, and multimodal
features that may increase motivation and engagement (Quizlet, n.d.; Cram, n.d.-a; Zeitlin &
Sadhak, 2023). Empirical research supports the motivational value of these design elements:
Sailer et al. (2017) found that specific gamification components, such as points, badges, and
leaderboards, enhanced learners’ intrinsic motivation by satisfying key psychological needs for
competence, autonomy, and relatedness. For instance, recent studies have provided empirical
evidence of Quizlet’s effectiveness in various educational contexts. Fursenko et al. (2021)
demonstrated that Quizlet significantly improved vocabulary retention and speaking proficiency
in an aviation English course, with students in the Quizlet-based learning group outperforming
traditional learners by 22.4%. Similarly, Özdemir and Seçkin’s (2024) meta-analysis of 23
studies found that Quizlet had a moderate-to-large positive effect on vocabulary retention
(Hedges’ g = 0.74) and a moderate effect on overall learning outcomes (Hedges’ g = 0.62).

55

Additionally, digital tools like Anki can help automate spaced repetition, optimizing
review schedules based on retrieval performance (Rana et al., 2020). However, while digital
flashcards enhance efficiency and accessibility, their effectiveness may be influenced by a
learner’s ability to manage potential distractions and the absence of tactile engagement, which
could impact cognitive encoding and recall.
JOLs
JOLs refer to learners’ ability to evaluate their mastery of material, which plays a critical
role in guiding effective study strategies. Accurate JOLs help students allocate study time
efficiently, whereas overconfidence or inaccurate judgments can lead to ineffective preparation
and poor retention (Nelson & Dunlosky, 1991).
Koriat’s (1997) cue-utilization framework explains that JOLs are influenced by three
primary cues: intrinsic (item difficulty), extrinsic (study conditions), and mnemonic (internal
retrieval fluency). Research suggests that while intrinsic and mnemonic cues strongly affect
JOLs, extrinsic factors, such as study repetition, are often discounted, leading students to
misjudge the effectiveness of spaced practice and retrieval-based learning (Koriat, 1997). This
bias contributes to students’ preference for passive techniques, such as rereading, over more
effortful but effective strategies like retrieval practice (Carpenter et al., 2022).
Retrieval practice and spaced repetition improve JOL accuracy by reinforcing memory
traces and revealing knowledge gaps through feedback (Koriat, 1997; Nelson & Dunlosky,
1991). The delayed-JOL effect, where JOLs made after a time delay are significantly more
predictive of actual recall, demonstrates how retrieval-based strategies enhance metacognitive
monitoring and self-regulated learning (Nelson & Dunlosky, 1991). Furthermore, delayed JOLs

56

reduce the impact of short-term memory interference, making them more reliable indicators of
retention over time (Tiwari, 2015).
Digital learning tools that incorporate spaced retrieval algorithms can further improve
JOL accuracy by offering immediate feedback on retention performance, helping learners
prioritize study efforts effectively (Yuan, 2022). However, overconfidence in unstructured study
environments remains a challenge (Higgins, Frankland, & Rathner, 2021). The presence of
multimodal information, such as text combined with images or audio, can also introduce a
cognitive load, sometimes leading to overestimated JOLs and increased overconfidence in digital
learning environments (Chen & Fu, 2003).
Misconceptions about learning, particularly the belief that passive strategies like
rereading are more effective, contribute to inaccurate JOLs (Anstead, 2022). These
misjudgments cause students to allocate study time inefficiently, underestimating the benefits of
retrieval-based learning and spaced repetition, ultimately hindering metacognitive accuracy and
long-term retention (Carpenter et al., 2022). To improve learning outcomes, interventions should
focus on enhancing students’ metacognitive awareness of effective study techniques while
promoting structured, feedback-driven learning environments that support retention,
comprehension, and application of knowledge.
Test Anxiety
Test anxiety significantly affects academic performance by increasing stress and
impairing cognitive functions (Hembree, 1988). In many post-secondary courses, students face
the challenge of comprehending, retaining, and integrating substantial volumes of information
across multiple courses each semester. This demanding academic context, often complicated by
overlapping test and assignment due dates, creates an environment conducive to heightened test

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anxiety (Rana & Mahmood, 2010; see also Gerwing et al., 2015). Rana and Mahmood (2010)
reported a significant negative correlation between students’ achievement scores and their levels
of test anxiety, underscoring the importance of cognitive factors, particularly worry, in contrast
to affective factors such as emotional distress. Their research posited test anxiety as a
contributing factor to suboptimal performance and academic underachievement, emphasizing the
potential of targeted interventions to provide students with effective coping strategies.
Structured study tools like flashcards may help mitigate anxiety by promoting a sense of
control and systematic preparation (Rana et al., 2020). Research suggests that flashcard-based
strategies, particularly those incorporating spaced repetition and retrieval practice, can enhance
students’ confidence in their ability to recall information under pressure, ultimately reducing
anxiety levels. Empirical evidence supports this connection, with studies showing that structured
flashcard use is linked to lower test anxiety, particularly among first-year university students —
for instance, 60.3% of first-year medical students reported reduced anxiety after incorporating
flashcards into their study routine (Rana et al., 2020).
Accurate JOLs, reinforced through retrieval practice, may also play a crucial role in
reducing test anxiety by helping students better assess their preparedness and allocate study time
effectively. Instructors can further contribute to alleviating test anxiety by implementing explicit
test orientations, encouraging positive thinking, fostering peer support networks, and
recommending relaxation training (Rana & Mahmood, 2010). Gerwing et al. (2015) additionally
suggested informing students about the commonality of test anxiety as a strategy to reduce
associated stigma, potentially enhancing students’ ability to seek and utilize available supports.
Taken together, these findings accentuate the imperative for examining and developing
additional structured interventions, such as flashcard strategies, that bolster students’ capacity to

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achieve intended learning outcomes and support efforts to manage test anxiety, particularly in
high-stakes academic settings where retrieval fluency and stress regulation are important.
Given the impact of test anxiety on academic performance, it is essential to explore
structured study interventions that not only enhance learning outcomes but also help manage
stress. Flashcard-based strategies, particularly those incorporating retrieval practice and spaced
repetition, have been shown to foster confidence, improve retention, and provide students with a
sense of control over their study process (Rana et al., 2020). However, while existing research
highlights the benefits of flashcards in reducing anxiety, questions remain about the optimal
ways to implement these strategies across different learning modalities, whether through
traditional paper-based methods or digital platforms.
To address this gap, the current research investigates how various flashcard-based
learning techniques influence retention, metacognitive accuracy, and test performance in
postsecondary students. By systematically examining retrieval practice, spaced repetition, and
elaborative encoding across different formats, these experiments aim to identify the most
effective approaches for optimizing learning, self-regulated study behaviours, and anxiety
reduction.
Research Objectives
This chapter presents findings from three interconnected experiments examining the
effectiveness of flashcard-based learning strategies in postsecondary education. Specifically, the
study explores (1) spaced versus retrieval practice in digital flashcards, investigating which
method yields superior retention and exam performance; (2) the effectiveness of the FlashcardsPlus strategy in paper-based flashcards, assessing whether elaborative encoding enhances
retention beyond traditional flashcard use; and (3) the integration of Flashcards-Plus with spaced

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practice in digital learning, exploring whether combining these techniques optimizes learning
outcomes, enhances metacognitive accuracy through JOLs, and reduces test anxiety.
Experiment 1 examines the relative benefits of spaced and retrieval practice within digital
flashcard platforms, with the primary objective of determining which method leads to better
retention and higher exam performance. Additionally, this experiment evaluates whether
students’ self-reported cramming behaviours (massed practice) negatively impact learning
outcomes. Although not its central focus, Experiment 1 also explores preliminary associations
between flashcard use and self-reported test anxiety, offering a baseline for comparisons in later
experiments.
Experiment 2 builds on these findings by evaluating the Flashcards-Plus strategy in a
traditional paper-based format. This method incorporates elaborative encoding through
paraphrasing and personalized examples and is expected to promote deeper cognitive
engagement and improve retention. Crucially, this experiment also measures students’ JOLs to
assess the accuracy of their metacognitive monitoring and whether enhanced encoding strategies
align perceived and actual performance.
Experiment 3 investigates whether students perceive benefits from using Flashcards-Plus
in combination with algorithmic spaced repetition, as implemented in Anki. Rather than directly
comparing flashcard types, the study focuses on the relationship between students’ perceived
helpfulness of flashcards and their academic performance, while assessing test anxiety and
judgment-of-learning (JOL) accuracy. The experiment aims to determine whether students who
report greater perceived usefulness of structured flashcard strategies also report lower anxiety
and perform better academically, thereby informing the potential value of adaptive digital tools
for supporting metacognitive monitoring and self-regulated learning in postsecondary settings.

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Findings from these experiments will contribute to a deeper understanding of how
evidence-based learning strategies interact with cognitive and neural mechanisms to optimize
student performance. However, their effectiveness is highly dependent on implementation
strategies. Structured approaches, such as the Flashcards-Plus strategy, offer an efficient way to
integrate retrieval practice and spaced repetition into study routines, maximizing long-term
retention and academic success.
Together, these experiments provide a comprehensive understanding of how retrieval
practice, spaced repetition, and elaborative encoding interact across different learning modalities.
While Experiment 2 focuses on traditional learning environments, Experiment 3 examines how
technology-assisted flashcards optimize learning through adaptive scheduling (Roediger &
Butler, 2011; Cepeda et al., 2006). By systematically evaluating these strategies, this research
bridges cognitive psychology with educational practice, offering actionable insights for
educators seeking to enhance student retention, metacognitive self-awareness, and academic
success (Dunlosky et al., 2013; Brown-Kramer, 2022). Addressing these research objectives
sequentially ensures a logical progression of insights, allowing each experiment to build on prior
findings and contribute to a cohesive framework for effective study strategies. Although referred
to as experiments, they are better understood as quasi-experimental investigations due to the
absence of fully randomized control groups and limited control over study conditions. This
classification reflects the naturalistic educational contexts in which they were conducted and
acknowledges the pragmatic constraints of classroom-based research.
Ethics Approvals
All three studies were conducted in line with the ethical principles of research. Research
ethics approvals were obtained from both UNBC (approval number 6009020 – Appendix 2) and

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CNC (approval number EO-2024-0508-102 – Appendix 3) in accordance with the Panel on
Research Ethics guidelines (2023). The students in both institutions were informed about the
nature and the purpose of the research after their final exam and provided with a data release
consent form with the possibility to opt out of their data being used for the study. Students who
gave their consent were provided with a paper-based survey in UNBC and a web-based survey in
CNC that assessed their study habits and post-test anxiety levels using the Westside Test Anxiety
Scale.
To ensure that participation did not influence grading outcomes, data analysis was
postponed until two weeks after students’ final grades had been submitted. Confidentiality and
anonymity have been maintained for all participants by anonymizing all responses after the
student grades were associated with survey responses. Data confidentiality was strictly
maintained in accordance with institutional and ethical guidelines. Identifiable information was
not collected, and all data were stored securely.
Additionally, several safeguards were implemented to mitigate any potential conflict of
interest arising from the Principal Investigator’s dual role as both researcher and course
instructor. Teaching Assistants, rather than the instructor, assessed all flashcard assignments
throughout the semester. Furthermore, following the final exam, research assistants, not the
course instructor, distributed and collected the research surveys. These measures ensured the
researcher’s non-involvement in grading and data collection processes, thereby maintaining the
integrity and impartiality of the research outcomes.

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Experiment 1: Spaced Practice vs. Retrieval Practice
Method
Participants. A total of 144 students enrolled in PSYC 102 Introduction to Psychology
course at UNBC in the winter semester of 2023 were randomly assigned to one of the
experimental conditions: 1) retrieval practice group (n = 72) where the students were instructed
to use Quizlet flashcards to prepare for their exams, and 2) spaced practice group (n = 72) where
the students were instructed to use Cram flashcards to prepare for their exams. Among the
students, 107 participants provided their consent for their data to be used for the study. To ensure
completeness, students with missing exam scores, whether from one of the midterms or the final
exam, were excluded from the analysis, resulting in the removal of nine participants from the
dataset. The resulting course performance data from 98 students (Quizlet group: n = 47; Cram
group: n = 51) were analyzed. Table 2 provides an overview of the demographic characteristics
of the study participants.

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Table 2
Experiment 1 Demographics
Demographic Variable

Description

Sample Size (N)

98

Mean Age (years)

20.18 (SD = 3.75)

Age Range (years)

17 - 46

Sex Distribution

Female = 73, Male = 25

Gender Distribution

Female = 71, Male = 25, Non-Binary = 1, Gender Fluid = 1

Degree Programs

Bachelor of Science (n = 52), Bachelor of Health Sciences (n = 30),
Other (n = 11)

Most Common Major

Psychology (n = 40)

Experimental Conditions

Cram (n = 51), Quizlet (n = 47)

Note. Experiment 1 Demographics.

Procedures. Quizlet (quizlet.com) and Cram (cram.com) were selected as the digital
platforms for this study, representing two distinct self-testing approaches: retrieval practice and
spaced practice. Both tools are well-established for supporting learning through flashcards,
leveraging key cognitive strategies such as active recall and spaced repetition to enhance
memory retention and conceptual understanding (Özdemir & Seçkin, 2024; Fursenko et al.,
2021). While Quizlet and Cram were selected to represent retrieval and spacing, respectively,
both platforms incorporate elements of each strategy. Quizlet includes adaptive features that
space review, and Cram still involves active recall through its flashcard interface. Thus, the
comparison reflects differences in design emphasis rather than pure strategy implementation.
To maintain consistency across experimental conditions, identical flashcard content was
used on both platforms. For instance, a Cram flashcard in this study featured the term “affective

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forecasting” on one side and its definition, “ability to predict our own and others’ happiness,” on
the reverse. By leveraging spaced repetition principles, Cram provides an efficient, structured
learning tool that integrates evidence-based cognitive strategies for long-term retention (Cram,
n.d.-a; Serfaty & Serrano, 2020).
The flashcards for both platforms were pre-designed by the course instructor to align with
the textbook chapters and lecture materials. These flashcards included key terms and definitions
deemed critical for understanding the course content and succeeding on the exams (Appendix 4).
The PSYC 102 Introduction to Psychology course included four equally weighted
examinations: three midterms and one final exam. Each exam comprised four matching
questions and 36 multiple-choice questions, all directly assessing terminology and definitions
that students practiced using flashcards. The exams each covered two textbook chapters,
focusing on the following topics: (1) the history of intelligence testing and the definition of
intelligence, (2) developmental psychology, (3) theories of emotion and motivation, (4) stress
and health, (5) social psychology, (6) personality theories, (7) psychological disorders, and (8)
psychological and biological therapies (Lilienfeld et al., 2022). These topics, drawn from
Chapters 9–16 of the course textbook, were reinforced through lectures that closely followed the
assigned readings. To ensure consistency, identical flashcard content, aligned with the textbook
chapters and lecture material, was used across both the Quizlet and Cram platforms.
Students were randomly assigned to one of two groups: the Quizlet group (n = 72) or the
Cram group (n = 72). Before each exam, both groups received a link to pre-made flashcards on
their respective platforms. These flashcards contained instructor-selected key terms and
definitions from the relevant textbook chapters and were intended to support exam preparation.
Students were instructed to use the flashcards to practice and were required to submit screenshots

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of their practice sessions as part of their class participation grade, which accounted for 5% of the
total course grade. Students received full participation marks for submitting the screenshots,
regardless of the frequency or duration of practice.
After completing the final exam, students were informed about the research study and
provided with a data release consent form. Those who consented participated in a paper-based
survey that assessed their perceptions of the flashcard assignments, study habits, and test anxiety
levels using the Westside Test Anxiety Scale (Driscoll, 2007). Nine participants with missing
exam scores (either one of the midterms or the final exam) were removed from the analysis. The
resulting course performance data from 98 students (Quizlet group: n = 47; Cram group: n = 51)
were included in the analysis. These data were exported from the Learning Management System
(LMS) Moodle, while a research assistant entered survey responses into an Excel spreadsheet.
Both datasets were then processed in R and statistically analyzed using R and Jamovi to evaluate
the efficacy of the self-testing strategies and the effect of other factors. Only a small minority of
students, ranging from 6 to 8, indicated they had not used flashcards to prepare for a particular
exam. At the same time, the overwhelming majority reported using them consistently across all
four assessments.
Results
Test Scores. A Multivariate Analysis of Variance (MANOVA) was conducted to
examine whether the flashcard condition (Quizlet vs. Cram) significantly affected students’ exam
performance across multiple assessments, including Midterm 1, Midterm 2, Midterm 3, and the
Final Exam. Results indicated a statistically significant multivariate effect of flashcard condition
on exam performance, Wilks’ Λ = 0.51, F(4, 94) = 22.61, p < .001, partial η² = .49, suggesting
that the type of study method (Quizlet vs. Cram) significantly influenced students’ exam

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performance across multiple assessments, confirming a meaningful multivariate effect. MSE is
not being reported for the MANOVA because it is not commonly used in multivariate tests like
Wilks’ Λ, which focus on overall differences across several dependent variables rather than
analyzing each one separately, like in ANOVA.

Table 3
Means, standard deviations, and results of MANOVA models for Quizlet and Cram groups
Total

Quizlet Group

Cram Group

(N = 98)

(n = 47)

(n = 51)

p-value

Partial η²

Midterm 1 grade

74.39 (12.11)

75.96 (11.34)

72.94 (12.72)

.220

.016

Midterm 2 grade

68.65 (12.81)

68.88 (13.8)

68.43 (11.97)

.863

< .001

Midterm 3 grade

68.37 (14.88)

68.72 (14.93)

68.04 (14.97)

.821

< .001

Final Exam grade

65.99 (14.61)

66.44 (13.98)

65.59 (15.29)

.776

< .001

Note. Descriptive statistics and MANOVA results for exam performance across flashcard
conditions. Both p-values and partial η² are reported in this table in accordance with APA
recommendations to convey the statistical significance and the magnitude of the effect.

A follow-up one-way Analysis of Variance (ANOVA) was conducted to determine
whether the flashcard condition (Quizlet vs. Cram) had a significant effect on students’ exam
performance, averaging across all three midterms and the final exam. Results revealed no
statistically significant difference between the Quizlet group (M = 70.00, SD = 11.91) and the
Cram group (M = 68.75, SD = 10.98) in terms of their overall exam performance, F(1, 96) =
0.29, MSE = 3642.10, p = .59. These findings suggest that, while the MANOVA revealed a
significant multivariate effect across individual exams, there was no significant difference when
considering the overall average exam performance between the two groups.
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To further explore differences in individual exams, a Mann-Whitney U test was
conducted instead of a standard independent-samples t-test due to the substantial imbalance in
sample sizes between groups. Specifically, the number of students who reported not using
flashcards to prepare for any of the four exams was consistently much smaller than that of
students who reported using flashcards, leading to unequal variances across exams. Unlike the
standard t-test, the Mann-Whitney U test is a non-parametric alternative that does not assume
normality, making it the most appropriate statistical approach for this analysis.
For Midterm 1, students who used flashcards (M = 74.58, SD = 12.25) had slightly higher
scores than those who did not (M = 72.19, SD = 10.89); however, this difference was not
statistically significant, U = 412.5, p = .498. A similar pattern was observed for Midterm 2,
where students who used flashcards (M = 68.83, SD = 12.39) scored slightly higher than nonusers (M = 65.83, SD = 19.54), but the difference remained non-significant, U = 294.5, p = .789.
Likewise, Midterm 3 scores were nearly identical between flashcard users (M = 68.37, SD =
14.93) and non-users (M = 68.33, SD = 15.38), with no significant difference, U = 276.0, p =
1.000. However, for the Final Exam, students who used flashcards (M = 67.25, SD = 14.00)
scored significantly higher than those who did not (M = 51.88, SD = 14.68), U = 560.0, p = .009,
indicating a significant advantage for flashcard users.

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Table 4
Mann-Whitney U Test Results with Medians and Interquartile Range (IQR) for flashcard users
and non-users
Mdn

IQR

Mdn

IQR

(users)

(users)

(non-users)

(non-users)

U-statistic

p-value

Midterm 1

77.50

16.88

72.50

11.88

412.5

.498

Midterm 2

70.00

17.50

67.50

27.50

294.5

.789

Midterm 3

68.75

21.25

68.75

21.25

276.0

1.000

Final Exam

67.50

20.00

52.50

17.50

560.0

.009

Note. Medians and interquartile ranges (IQRs) are reported for each group to reflect central
tendency and variability. The Mann-Whitney U-statistic and corresponding p-values are included
to indicate the significance of group differences.

Therefore, flashcard use did not significantly impact performance on the first three
midterms, but a significant benefit emerged for the Final Exam. These findings suggest that
flashcards may facilitate long-term retention rather than providing an immediate advantage for
short-term exam preparation. The extended time frame for preparing for the Final Exam may
have allowed students to fully benefit from spaced repetition and retrieval practice, which are
key cognitive mechanisms underlying the effectiveness of flashcard-based studying.
Test Anxiety. A Spearman’s rank-order correlation analysis was conducted to examine
the relationship between students’ test anxiety levels and their final course grades. The Westside
Test Anxiety Scale scores were compared with students’ actual final grades to assess the strength
and direction of the association.
The analysis yielded a non-significant correlation, rs = 0.002, p = .98, indicating no
meaningful relationship between test anxiety levels and final course grades. These findings
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suggest that students with higher test anxiety scores did not systematically achieve lower
academic performance compared to their peers.
This finding aligns with prior research, which highlights that mild to moderate levels of
anxiety may not substantially impair performance (Driscoll, 2007; Connor et al., 2016). Given
the negligible effect size, test anxiety does not appear to be a strong predictor of academic
outcomes in this experiment.
JOL Accuracy. Descriptive analysis revealed that students’ expected final course grades
(M = 73.03, SD = 14.24) were very close to their actual final course grades (M = 73.57, SD =
11.87). Expected grades ranged from 2.90% to 100.00%, while actual final grades ranged from
33.28% to 96.06%.
A Spearman’s rank-order correlation was conducted to examine the relationship between
students’ self-reported expected grades and their actual final grades. The analysis yielded a weak
positive correlation, rs = 0.17, p = .09. This result was not statistically significant.
These findings are partially consistent with prior research, which highlights that while
optimistic self-assessments may align with academic success in some cases, other factors, such
as study strategies, motivation, and anxiety, play a critical role in shaping actual performance
(Zimmerman, 2002; Koriat & Bjork, 2005). Students with more accurately calibrated JOLs may
demonstrate better self-regulation and academic outcomes. However, given the non-significant
correlation observed in this dataset, other influences beyond students’ initial expectations may
have contributed to their final performance.
Cramming Tendencies. Students’ self-reported cramming behaviour, measured on a
scale from 1 (“never”) to 5 ("all the time"), had a mean of 3.66 (SD = 1.10), suggesting that most
students reported cramming behaviour “quite often” or “50% of the time.” Final course grades

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averaged 73.51% (SD = 11.94), while test anxiety scores, assessed on a scale from 1 to 5, had a
mean of 3.22 (SD = 0.74), indicating moderate levels of test anxiety among participants.
A Spearman’s rank-order correlation analysis was conducted to explore the relationships
among cramming behaviour, final grades, and test anxiety. The results revealed a non-significant
correlation between cramming and final grades, rs = 0.18, p = .09. Similarly, a non-significant
correlation was found between cramming and test anxiety, rs = 0.12, p = .23.
None of the correlations between cramming behaviour, test anxiety, and final course
grades reached statistical significance. These findings align with prior research suggesting that
cramming may not inherently harm or improve academic outcomes but may contribute to shortterm stress (Hartwig & Dunlosky, 2012). Given that none of these correlations reached statistical
significance, future research could explore alternative study habits, such as distributed practice,
and investigate whether structured interventions targeting test anxiety and study strategies could
enhance academic performance.
The findings from the first experiment provided insights into the effectiveness of
different digital flashcard platforms (Quizlet vs. Cram) in enhancing student learning. While both
methods facilitated final exam performance, no significant differences emerged between the two,
suggesting that the overall benefit of flashcards may stem from their active recall and repetition
components rather than the specific platform used. This outcome implies that while both retrieval
practice and spaced repetition are effective, neither strategy appeared to outperform the other
when implemented through standard platform settings, possibly due to overlapping features or
inconsistent engagement with the intended algorithms. Additionally, the role of study habits,
cramming behaviours, and test anxiety did not strongly predict final course grades, indicating

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that other factors, such as the structure of the study materials, may play a crucial role in learning
outcomes.
Building upon these results, Experiment 2 was designed to test the hypothesis that an
enhanced flashcard method, Flashcards-Plus, would improve retention and metacognitive
accuracy compared to traditional paper-based flashcards. The Flashcards-Plus condition
introduces additional metacognitive prompts, such as paraphrasing and generating personal
examples, designed to improve self-regulated learning by enhancing metacognitive monitoring
and control over encoding strategies. In contrast, the Traditional Flashcards condition reflects
conventional study practices that emphasize rote memorization, which may support basic recall
but offer limited benefits for higher-order cognitive processes like metacognitive awareness,
elaborative encoding, and strategic self-regulation. This experiment aimed to determine whether
modifying flashcard structure and encouraging deeper engagement with study materials can lead
to greater retention and academic success.
Experiment 2: Flashcards-Plus vs Traditional Flashcards in Paper-Based Format
Method
Participants. Participants in Experiment 2 included 40 students enrolled in PSYC 319
Philosophy of Mind course at UNBC during the winter semester of 2023. Among these, 28
students consented to participate in the research by signing the data release consent form. The
sample had a mean age of approximately 21.3 years (SD = 3.8), with ages ranging from 18 to 42
years. Gender distribution included primarily female participants (n = 18) and male participants
(n = 10). Most participants were pursuing Bachelor of Arts degrees with a major in Psychology
(n = 17), while others were enrolled in interdisciplinary programs or related fields.

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This sample was selected to explore the impact of different flashcard-based study
methods on academic performance, with students engaging in both traditional and FlashcardsPlus strategies as part of the course curriculum. Demographic data and survey responses were
collected to provide insights into the role of study habits, anxiety, and test performance across
the study population.
Materials. The PSYC 319 Philosophy of Mind course utilized Ravenscroft’s (2005)
textbook as the required reading. Course lectures closely followed the textbook and covered four
main topics: (1) Mental States (chapters 1–5), (2) Mind as Machine (chapters 6 and 7), (3) Mind
in a Physical World (chapters 8–10), and (4) Consciousness (chapters 11 and 12). These topics
provided a comprehensive foundation for understanding contemporary debates in philosophy of
mind.
To support their learning, students were supplied with 3x5-inch white index cards to
create their flashcards. For example, a typical flashcard demonstrated by the instructor in the
PowerPoint presentation might have had the term “Epiphenomenalism” written on one side, with
the definition “a version of property dualism that insists that mental properties do not causally
impact upon the brain” on the reverse. This approach ensured uniformity in the materials while
allowing for individual content customization.
Procedures. The course assessments comprised three equally weighted “knowledge
tests” (15% each), analogous to two midterms and one final exam. Each test was a closed-book,
in-class assessment that covered approximately one-third of the course material. All assessments
were designed to evaluate students’ conceptual understanding, factual recall, and ability to
articulate philosophical arguments in written form.

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Knowledge Test I assessed chapters 1–4 and consisted of 40 questions: 30 multiplechoice questions (1 point each), five fill-in-the-blank items (1 point each), three true/false items
(1 point each), and two short-answer questions (6 points each). Knowledge Test II covered
chapters 5–8 and included 30 questions: 26 multiple-choice items (1 point each) and four shortanswer questions (6 points each). Knowledge Test III examined chapters 9–12 and included 30
questions: 26 multiple-choice items (1 point each), one true/false question (1 point), and three
short-answer questions (5 points each).
Before the first test, all students were given a list of instructor-selected key terms and
definitions, along with blank index cards. Students created paper-based flashcards using the
provided materials as part of the lecture. The terms and definitions on the list were derived from
the textbook and lecture content, focusing on critical concepts necessary for understanding and
exam success. This ensured consistency in the flashcards produced by the students, providing a
uniform study aid for the first midterm exam.
For the second test, the same process was followed with one critical difference: students
were instructed to use the Flashcards-Plus strategy. This method required students to paraphrase
the provided definitions in their own words and generate personalized examples for each key
term. These tasks were completed during class time, and students were encouraged to use their
custom flashcards for exam preparation. The Flashcards-Plus strategy aimed to deepen cognitive
engagement and promote active learning by requiring students to process the material at a more
personalized and meaningful level.
No specific instructions or pre-defined lists of terms were provided for the final exam.
Students were instead encouraged to develop their flashcards independently using their preferred
methods. Paper-based index cards remained available for students to use, but the approach to

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flashcard creation was left to their discretion. Subsequently, 17 students reported using
flashcards to prepare for the final knowledge test, while 11 students indicated they did not use
them.
After completing the final test, students were informed about the study by a research
assistant and invited to participate by signing a data release consent form. Similarly to the first
experiment, this was done by a research assistant in the absence of the course instructor, who
was also the Principal Investigator in the study, in order to mitigate any potential conflict of
interest and ensure the integrity and impartiality of the research outcomes.
Students who agreed to participate completed a paper-based survey that gathered
information on their perceptions of the flashcard assignments, study habits, and levels of test
anxiety, assessed using the Westside Test Anxiety Scale (Driscoll, 2007). Data analysis was
delayed for two weeks until after students’ final course grades were entered into the system and
made available to them before the analysis began.
The resulting course data from 28 consenting students were included in the analysis.
Performance data were exported from the LMS (Moodle) into Excel, while a research assistant
manually entered survey responses into an Excel spreadsheet. Both datasets were subsequently
imported into R for preprocessing and analyzed statistically using Jamovi. These analyses
examined the effectiveness of the flashcard assignments and explored their relationships to
students’ academic performance, study strategies, and perceptions of the learning process.
Planned statistical analyses included nonparametric methods such as Friedman’s test to
compare repeated exam scores across flashcard conditions (traditional vs. Flashcards-Plus)
among consistent users, as well as Spearman’s rank-order correlations to assess the relationships
between metacognitive measures (JOLs), test anxiety scores, and academic performance. The

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primary dependent variables were knowledge test scores, test anxiety (Westside Scale), and selfreported study behaviours. The independent variables included flashcard strategy (traditional vs.
Flashcards-Plus) and flashcard usage (yes/no), as defined by student condition and self-report.
These analyses were designed to address the study’s broader purpose of evaluating how
elaborative encoding and metacognitive scaffolding influence learning outcomes, particularly in
relation to self-regulation, retention, and anxiety in high-stakes assessment contexts.
Results
Test Scores. The analysis focused on students who consistently reported using flashcards
for all three assessments (n = 14) to evaluate the impact of flashcard-based study strategies on
academic performance. This selection reduced variability from inconsistent study habits and
allowed for a clearer examination of whether regular flashcard use influenced test performance
over time.
The mean score for Knowledge Test 1, which was prepared using instructor-provided
definitions and paper-based flashcards, was M = 74.86% (SD = 9.31). For Knowledge Test 2,
which incorporated the Flashcards-Plus strategy, the mean score was M = 75.10% (SD = 7.38),
suggesting minimal change despite a strategy shift that encouraged deeper cognitive engagement
and personalized learning. By Knowledge Test 3, the mean score decreased to M = 71.77% (SD
= 10.05), reflecting a slight decline in performance over time.
A Friedman’s test was conducted to determine whether there were significant differences
in exam scores across Knowledge Test 1, Knowledge Test 2, and Knowledge Test 3 among
students who consistently used flashcards for all three tests (n = 14). Due to the small sample
size and potential violations of normality, Friedman’s test was chosen as a non-parametric
alternative to repeated-measures ANOVA. While MSE is not typically required in APA-style

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MANOVA reporting, its inclusion follows the recommendations of Tabachnick and Fidell
(2007), who emphasize its value in understanding model error and precision. Including MSE
helps enrich the interpretation of the multivariate results by offering insight into the residual
variance associated with each dependent variable. Specifically, the MSE values for Knowledge
Test 1, Knowledge Test 2, and Knowledge Test 3 were 140.29, 66.39, and 201.00, respectively,
reflecting varying levels of residual error across the three assessments.
The results of Friedman’s test did not indicate a statistically significant difference in
performance across the three tests, χ²(2) = 1.00, p = .607, suggesting that students’ scores
remained relatively stable despite consistent flashcard use. To further assess the robustness of
these findings, a bootstrapped Friedman’s test (10,000 resamples) was conducted. The
bootstrapped mean Friedman statistic was 2.98, with a 95% confidence interval ranging from
0.14 to 10.43. The wide confidence interval suggests high variability in the resampled
distributions, likely due to the small sample size, which may have limited the statistical power to
detect meaningful differences.
The findings indicate that consistent flashcard use did not lead to significant
improvements or declines in test performance over time. While flashcards are widely recognized
as an effective study tool, their impact may depend on additional factors such as study duration,
test format, or complementary learning strategies. The bootstrapped results further confirm that
the observed differences in test scores were small and inconclusive, suggesting that a larger
sample size may be necessary to determine the actual effects of flashcard-based study strategies.
Test Anxiety. A Spearman’s rank-order correlation analysis was conducted to examine
the relationship between students’ test anxiety levels and their final course grades. The Westside

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Test Anxiety Scale scores were compared with students’ actual final grades to determine the
strength and direction of the association.
The analysis yielded a Spearman’s correlation coefficient of -0.12 (p = .45), indicating a
non-significant negative relationship between test anxiety levels and final grades. These findings
suggest that students with higher test anxiety scores did not exhibit significantly lower academic
performance compared to their peers, consistent with prior research indicating that mild to
moderate levels of anxiety may not necessarily impair performance (Driscoll, 2007; Connor et
al., 2016).
JOLs. To examine students’ JOLs, the analysis focused on the relationship between their
self-reported expected final course grades and their actual final grades. Descriptive statistics
revealed that students’ mean anticipated grades (M = 74.54, SD = 7.61) were slightly lower than
their actual final grades (M = 76.36, SD = 6.98). This pattern of underestimation suggests a slight
tendency toward conservative estimates, contrasting with the common trend of overestimation
found in prior studies (Koriat & Bjork, 2005; Son & Metcalfe, 2005).
To investigate this relationship further, Spearman’s rank-order correlation assessed the
association between students’ expectations and their actual performance. The analysis yielded a
strong, positive correlation (rs = 0.686, p < .001), indicating that students with higher
expectations tended to achieve higher final grades compared to their peers. This finding aligns
with prior research emphasizing the link between optimistic self-assessments and academic
performance (Zimmerman, 2002).
In addition, students who accurately calibrated their judgments (i.e., those with smaller
discrepancies between expected and actual grades) exhibited better overall performance,
consistent with theories of self-regulated learning. Future research should explore whether

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interventions targeting metacognitive awareness might further align students’ JOLs with their
actual outcomes, potentially enhancing academic success.
The findings from Experiment 2 suggest that modifying flashcard design to include
metacognitive prompts (Flashcards-Plus) may support students in monitoring their learning.
However, without structured guidance, the long-term impact on retention and performance
remains unclear. Notably, students in this study exhibited a conservative bias in their JOLs,
underestimating their actual performance, a pattern that contrasts with prior research suggesting
a tendency toward overestimation. Furthermore, the strong positive correlation between expected
and actual grades suggests that students with higher self-expectations generally achieve better
academic outcomes, reinforcing the link between self-assessment accuracy and performance.
However, the results also indicate that additional factors, such as study strategies and test
anxiety, may contribute to learning outcomes beyond self-expectations alone.
Building on these findings, Experiment 3 explores whether integrating Flashcards-Plus
features into a digital format can enhance learning outcomes and self-regulation. Although the
third experiment does not directly compare Flashcards-Plus with traditional flashcards through
statistical testing, it investigates whether incorporating elaborative encoding techniques within
digital tools aligns with students’ performance, metacognitive awareness, and anxiety. The aim is
to evaluate how specific design features in digital flashcards influence academic outcomes and
study behaviours.
Experiment 3: Flashcards-Plus in Digital Format
Method
Participants. The third experiment was conducted at two post-secondary institutions:
UNBC and CNC (the participants are the same participants who took part in the study described

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in Chapter 4 of this dissertation). The sample consisted of 40 participants (12 male and 28
female; mean age = 23) enrolled in introductory psychology courses at these institutions. Of
these, 14 participants were native speakers of English, while the remaining participants spoke
one of India’s official languages or dialects.
At UNBC, the study involved 18 participants who attended face-to-face courses. The
gender distribution at UNBC included 14 females and four males. Participants’ ages ranged from
18 to 30 years, with an average age of approximately 20.6 years (SD = 3.07). Table 5 details the
participants’ first and second languages by institution. At CNC, the study included 22
participants who attended an online course. The gender distribution included 16 females and six
males. Participants’ ages ranged from 19 to 44 years, with an average age of approximately 22.5
years (SD = 4.84).

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Table 5
L1 and L2 by Institution
Institution 1
L1

n spoken

English

7

Hindi

L2

Institution 2
n spoken

L1

n spoken

L2

n spoken

English

9

Punjabi

15

English

12

3

Hindi

3

English

7

Hindi

4

Gujarati

2

Urdu

3

Punjabi

1

Punjabi

1

Malaydam

2

None

7

Shina

1

Punjabi

2

Sindhi

1

Arabic

1

Tagalog

1

Burushaski

1

Urdu

1

Italian

1

Yoruba

1

None

4

Note. L1 and L2 by Institution.

Materials. At UNBC, the PSYC 101 Introduction to Psychology course included two
equally weighted in-class exams (midterm and final), each worth 30% of the final grade. These
closed-book assessments covered different portions of the course material and could include
multiple-choice and short-answer questions. In addition to exams, students completed weekly
quizzes and assignments (20%), which were typically open-book and could be delivered during
class as pop quizzes. These included essay-style and fill-in-the-blank questions based on lecture
content and textbook readings. A creative group project (20%) allowed students to present a
psychological concept using a format of their choice (e.g., video, podcast, cartoon, or debate).
Projects were evaluated on accuracy, clarity, creativity, and peer feedback. Students could also
earn up to 4% bonus credit through participation in departmental research studies.
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During Weeks 2 and 3, students received lists of instructor-selected key terms and
definitions aligned with the textbook and lecture content, focusing on essential concepts for
understanding and exam success. Following instructor guidance, students used these to create
their first set of Anki flashcards during class. In Weeks 3 and 4, students were introduced to the
Flashcards-Plus strategy, which required them to paraphrase definitions in their own words and
generate personal examples. A second set of flashcards was created in class, with guidance on
how to use them in preparation for the final exam.
At CNC, the PSYC 101 course was delivered asynchronously online, with assessments
including weekly quizzes and assignments (40%), a final exam (30%), and an individual concept
presentation (30%). Similar to UNBC, quizzes and assignments included open-book shortanswer and fill-in-the-blank questions, and could be based on textbook or lecture materials not
explicitly covered in both. For the final exam, students were tested on cumulative knowledge
using a mix of multiple choice, true/false, matching, fill-in-the-blanks, or essay-type questions.
The concept presentation allowed students to select and explore a major psychological concept
using a creative format, individually or with a partner. Presentations were assessed based on
clarity, conceptual accuracy, and presentation style.
The required textbook for both sections was the NOBA Psychology Collection (BiswasDiener & Diener, 2016). The flashcard content was aligned closely with the textbook and lecture
materials.
Procedures. This quasi-experimental design explored the relationship between perceived
flashcard helpfulness and academic outcomes in two postsecondary psychology courses.
Students at two institutions, UNBC and CNC, used Anki digital flashcards throughout the
semester, first in a traditional format and later with the Flashcards-Plus strategy. This design

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allowed for an investigation into whether students who engaged with elaborative encoding
perceived benefits in retention and test performance, although no direct statistical comparison
was made between the two strategies.
Unlike Experiments 1 and 2, which investigated the effects of structured flashcard
strategies on exam performance, Experiment 3 focused on students’ perceived usefulness of
flashcards as the key variable of interest. Evaluating students’ attitudes provided insight into how
learners engage with self-regulated learning tools in real-world academic settings. These
metacognitive evaluations are critical for understanding not only whether a strategy is effective,
but whether students recognize its value and are likely to adopt it independently—factors that
strongly influence long-term learning outcomes and strategy sustainability.
At UNBC, during weeks 2 and 3, students created Anki flashcards based on instructorprovided key terms and definitions, using the standard format of a concept on one side and its
definition on the reverse. The Flashcards-Plus strategy was introduced in weeks 3 and 4,
prompting students to paraphrase definitions and generate personalized examples. Students used
these flashcards throughout the course for weekly quizzes, midterms, and the final exam. At
CNC, students created flashcards using video instructions in weeks 1 to 5, employing the
traditional method. In weeks 6 to 10, they implemented the Flashcards-Plus strategy, again using
personalized flashcards for ongoing assessments.
At CNC, during weeks 1 to 5, students created Anki flashcards following video
instructions. They adopted the Flashcards-Plus strategy from weeks 6 to 10, using personalized
flashcards for quizzes, assignments, and the final exam.
The primary dependent variable was academic performance (quiz, assignment, and final
exam scores). The main independent variable was students’ perceived helpfulness of flashcards,

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as reported in the end-of-course survey. Planned statistical analyses included Spearman’s rankorder correlations to assess the relationship between perceived flashcard helpfulness and
academic performance outcomes. Analyses were conducted separately for each institution due to
differing instructional conditions. Consistent with previous experiments, data analysis was
delayed for two weeks following grade finalization to protect student confidentiality and reduce
bias.
Results
The collected data were analyzed to examine whether students who reported that
flashcard assignments helped them remember information better also performed better
academically. Spearman’s rank-order correlations were computed between students’ perceived
flashcard usefulness and their scores on quizzes and assignments, final exams, and overall course
performance.
For the CNC class, the Spearman correlation between flashcard helpfulness ratings and
students’ quizzes and assignment scores was not significant, rs(20) = −0.31, p = .16. The
correlation between flashcard helpfulness ratings and overall course performance was also not
significant, rs (20) = −0.06, p = .79. Additionally, the correlation between flashcard helpfulness
ratings and final quiz scores was not significant, rs(20) = 0.41, p = .058.
For the UNBC class, the correlation between flashcard helpfulness ratings and students’
quizzes and assignment scores was not significant, rs(16) = 0.35, p = .16. The correlation
between flashcard helpfulness ratings and final exam scores was not significant, rs(16) = −0.22, p
= .38, and the correlation with overall course performance was also not significant, rs(16) = 0.06,
p = .81.

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These results indicate no statistically significant relationships between students’
perceived flashcard usefulness and their academic performance in either class. The lack of
significant relationships suggests that external factors, such as study habits, test-taking strategies,
and metacognitive awareness, may play a greater role in determining academic success than
students’ perceptions of study tool effectiveness.
Conclusions
This research investigated the effectiveness of flashcard-based learning strategies by
examining the role of active recall, spaced repetition, and elaborative encoding in improving
academic performance, self-regulated learning, and test-related anxiety in postsecondary
education. Three interconnected experiments explored these cognitive techniques across digital
and paper-based modalities to assess their impact on exam performance, perceptions of learning,
and metacognitive accuracy.
Experiment 1 evaluated retrieval practice and spaced repetition using Quizlet and Cram
flashcards in a digital learning environment. While MANOVA results suggested a statistically
significant multivariate effect of flashcard condition on exam performance, further analysis
revealed no significant difference in overall exam performance between the two groups.
However, students who consistently used flashcards showed significantly higher final exam
scores, suggesting that flashcards may be particularly beneficial for long-term retention rather
than short-term exam preparation. Additionally, neither test anxiety nor cramming tendencies
strongly predicted final course grades, highlighting the need for structured interventions that
promote effective study habits.
Experiment 2 examined the effectiveness of the Flashcards-Plus strategy, which
incorporates elaborative encoding through paraphrasing and personalized examples, in a

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traditional paper-based format. The results indicated that while test performance remained stable
across different study methods, students exhibited a strong positive correlation between their
expected and actual final grades. This finding suggests that metacognitive awareness, rather than
specific flashcard techniques, may play a crucial role in academic performance. Additionally,
students in this study tended to underestimate their performance, contrasting with previous
research on overconfidence in self-assessments.
Experiment 3 assessed the Flashcards-Plus strategy in a digital format combined with
spaced repetition algorithms. The results were mixed, with no statistically significant correlation
between students’ perceived usefulness of flashcards and their academic performance at either
CNC or UNBC. This suggests that external factors such as study consistency, instructional
guidance, and course structure may influence the effectiveness of digital flashcard tools more
than students’ subjective perceptions of their utility.
Notably, test anxiety did not significantly correlate with academic outcomes in any
experiment, suggesting that mild to moderate anxiety may not impair performance. In contrast,
metacognitive awareness, as demonstrated by a strong positive correlation between expected and
actual grades in Experiment 2, was more predictive of academic success. However, this
relationship was not replicated in Experiment 3, where students’ perceived flashcard helpfulness
did not align with performance.
Across all three experiments, this research provides nuanced insights into the role of
retrieval practice, spaced repetition, and elaborative encoding in optimizing learning outcomes.
While flashcards facilitate long-term retention, their effectiveness appears to depend on
structured implementation, student engagement, and contextual factors such as study duration
and modality. The findings emphasize the importance of metacognitive awareness in self-

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regulated learning. They also highlight the need for further research on integrating digital
learning tools with structured instructional support to maximize their benefits in postsecondary
education.
Discussion
The current research offers both theoretical and practical insights into how flashcardbased learning strategies influence academic performance, self-regulated learning, and
metacognition in postsecondary education. By evaluating retrieval practice, spaced repetition,
and the Flashcards-Plus strategy across multiple learning modalities, this study provides a
nuanced understanding of how these cognitive techniques interact with different instructional
formats and study behaviours.
The findings from Experiment 1 reinforce the well-established cognitive benefits of
retrieval practice and spaced repetition (Roediger & Butler, 2011; Dunlosky et al., 2013).
Although the MANOVA revealed a significant multivariate effect of flashcard condition on
exam performance, follow-up analyses showed no significant difference in overall performance
between the Quizlet (retrieval) and Cram (spaced) groups. This may suggest that both platforms
offer overlapping cognitive benefits, possibly due to participants' unintentional use of both
strategies or limited adherence to each platform’s distinctive algorithms.
However, students who consistently used flashcards performed significantly better on the
final exam, suggesting that these techniques may be more beneficial for long-term retention
rather than short-term gains. This pattern may reflect the naturally extended preparation timeline
for final exams, which could amplify spacing effects regardless of the assigned platform (Cepeda
et al., 2006). Moreover, unmeasured individual differences, such as intrinsic motivation or

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conscientiousness, may have influenced both flashcard use and final outcomes, introducing
potential confounds (Deci & Ryan, 1985).
Experiment 2 emphasized the potential of elaborative encoding to enhance metacognitive
monitoring. However, despite strong correlations between students’ expected and actual grades,
there were no significant differences in test scores across the Flashcards-Plus and traditional
flashcard phases. This may suggest that elaborative prompts alone are insufficient without
ongoing guidance. Prior work suggests that elaboration benefits are maximized when paired with
immediate feedback or retrieval attempts (McDaniel, 2023). The decline in performance on the
third test could also reflect cognitive fatigue or increased difficulty of content, rather than
diminished effectiveness of elaborative encoding.
Moreover, the high JOL accuracy observed may reflect students’ prior academic
experience or domain familiarity rather than improved metacognitive calibration due to flashcard
type. Learners with greater topic familiarity tend to make more accurate JOLs, independent of
the strategies used (Koriat & Bjork, 2005). Thus, differences in students’ baseline knowledge or
self-efficacy may have contributed to observed outcomes.
Experiment 3 yielded no statistically significant correlation between students’ perceived
flashcard helpfulness and their academic performance; nevertheless, the findings provide
valuable insights into the complexities of digital learning environments. While this could imply a
disconnect between perceived and actual learning benefits, several alternative explanations merit
consideration. One possibility is that digital learning contexts impose distinct cognitive demands,
such as multitasking and susceptibility to distractions, which may reduce the efficacy of
otherwise well-designed tools (Chen & Fu, 2003). It is also plausible that external factors, such
as study habits, test-taking strategies, and metacognitive awareness, exert greater influence on

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academic success than students’ subjective perceptions of tool effectiveness. Additionally,
retrospective bias may have influenced students' self-ratings, such as hindsight bias following
grade feedback or social desirability during post-course surveys (Tversky & Kahneman, 1974).
Across all three experiments, test anxiety did not significantly predict academic
performance. While structured tools like flashcards have been proposed to reduce anxiety by
enhancing perceived preparedness (Rana et al., 2020), the null findings in this study may reflect
a restricted range of anxiety levels, particularly given the moderate average anxiety scores
observed. Moreover, contextual factors such as grading policies, exam format, or instructor
support may have mitigated test anxiety across groups, reducing its predictive value.
Lastly, metacognitive calibration was strongly associated with academic performance in
Experiment 2. The strong correlation between students’ grade expectations and their
performance underscores the importance of accurate self-assessment in learning (Koriat & Bjork,
2005; Zimmerman, 2002). However, this relationship may be bidirectional: students who
performed well might have retrospectively aligned their expectations with their results, rather
than demonstrating accurate foresight. Because the study did not include pretest JOLs, it remains
unclear whether improved calibration resulted from strategy use or simply a reaction to
performance feedback.
In sum, these findings support the cognitive value of flashcards and metacognitive
scaffolding but also underscore the importance of sustained engagement, contextual factors, and
learner variability. Together, these findings contribute to a deeper understanding of how retrieval
practice, spaced repetition, and elaborative encoding interact with different educational contexts.
While flashcards remain a highly effective learning tool, their success depends on consistent
engagement, structured implementation, and metacognitive support mechanisms.

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Implications and Future Directions
The present findings offer several implications for improving learning outcomes and
guiding future research on flashcard-based study strategies in postsecondary education. First,
retrieval-based learning strategies should be systematically integrated into curricula through
structured digital tools like Quizlet and Anki or through instructor-facilitated exercises, as the
findings across all three experiments demonstrated that consistent flashcard use, particularly
strategies emphasizing active recall, was associated with higher exam performance and stronger
metacognitive calibration, even when no single platform or technique consistently outperformed
others.
However, the results from Experiment 3 highlight that students’ perceptions of flashcard
effectiveness do not necessarily predict academic performance. This disconnect may reflect the
influence of retrospective bias, e.g., hindsight or social desirability effects (Tversky &
Kahneman, 1974) and contextual factors that shape students' evaluations after assessment rather
than during the learning process. This suggests that external factors, such as study habits, testtaking strategies, and metacognitive awareness, may play a more significant role in learning
outcomes than the perceived usefulness of digital study tools.
Second, the study underscores the importance of consistent guidance and scaffolding in
sustaining effective study habits over time. Experiment 2 showed that without structured support,
students may revert to passive learning behaviours, reducing the long-term benefits of
elaborative encoding, as evidenced by the lack of significant improvement in test performance
between the traditional and Flashcards-Plus phases, despite the latter involving deeper processing
through paraphrasing and example generation. This plateau in performance suggests that simply

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introducing elaborative prompts may not be sufficient unless reinforced through continued
instructional scaffolding and feedback.
To maximize learning outcomes, educators should incorporate explicit instructional
strategies that encourage active engagement with retrieval practice, spaced repetition, and
elaborative encoding. Providing structured guidance in implementing flashcard-based
techniques, especially those incorporating elaborative encoding, may help students maintain
effective study strategies over time and maximize long-term learning outcomes.
Third, these findings support the integration of elaborative encoding into self-testing
strategies, as it promotes not just memorization but also deeper comprehension and knowledge
application. The Flashcards-Plus strategy, which requires paraphrasing and personalized
examples, was associated with improvements in metacognitive calibration. Future studies may
also explore whether elaborative encoding within digital flashcard formats, such as FlashcardsPlus, yields measurable performance benefits compared to traditional digital flashcards, as this
distinction was not explicitly examined in the current experiment.
However, this relationship may be bidirectional: students who performed well might have
retroactively aligned their expectations with their actual outcomes rather than predicting them
accurately from the outset. Given that many students struggle with metacognitive accuracy,
future interventions could focus on enhancing self-monitoring strategies, such as immediate
feedback, reflection prompts, and adaptive learning technologies, to promote more precise JOLs.
Future studies should also include both pre- and post-test JOLs to distinguish between genuine
calibration and post hoc rationalization.
Additionally, the moderate test anxiety levels observed in this study, along with the lack
of significant predictive value, suggest that contextual or instructional variables may buffer

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students from the negative effects of test anxiety. Future research could examine whether
structured tools like flashcards are more effective in reducing anxiety under conditions of highstakes pressure, minimal support, or greater baseline anxiety.
Future research should explore how the combination of retrieval practice, spaced
repetition, and elaborative encoding can be optimized for diverse learning populations,
particularly among students with varying levels of metacognitive awareness. Additionally,
investigating how study frequency, structured retrieval practice, and instructional guidance
mediate the relationship between perceived effectiveness and actual learning outcomes would
further refine our understanding of these strategies’ impact on knowledge retention and transfer.
Given the variability in digital learning outcomes observed in Experiment 3, future
studies should examine how different instructional formats and scaffolding methods affect
engagement with digital flashcards. Adaptive learning technologies that personalize study
schedules based on individual performance may offer new insights into optimizing learning
efficiency in digital environments. Research should also consider the role of cognitive load,
working memory capacity, and retrieval difficulty in shaping the effectiveness of different study
techniques.
Finally, future studies should track platform-specific usage patterns more closely to
determine whether students engage with flashcard software as intended. Previous work has
shown that learners may bypass the core features of retrieval or spacing when using digital tools,
limiting their effectiveness (Karpicke et al., 2009). To improve internal validity, future studies
should incorporate more direct measures of engagement, such as log file tracking or usage
diaries, to determine how closely participants follow the intended study schedules and to better

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isolate the effects of spaced repetition. This highlights the need for better alignment between tool
design, instructional context, and student behaviour.
Future research should adopt more consistent measurement approaches to improve
comparability across experiments. In particular, combining perceived effectiveness ratings with
behavioural usage data, such as usage logs or structured study diaries, would offer a more
comprehensive view of student engagement. The methodological divergence in Experiment 3,
while necessary due to cross-institutional constraints, limited the ability to track flashcard use
directly and compare findings with Experiments 1 and 2. Greater alignment in data collection
would strengthen future interpretations of strategy effectiveness.
Although this study did not impose external controls on the timing or frequency of
flashcard use, students provided self-reported data on how often they practiced with each card
set. These usage data were not included in the main analyses to preserve the clarity and statistical
focus on platform-based comparisons. However, they offer a valuable avenue for future
secondary analyses. Investigating whether students who reported more frequent or distributed
practice achieved better performance could help clarify the influence of self-directed spacing
behaviour on learning outcomes.
Overall, this research provides empirical support for the potential effectiveness of
structured flashcard-based strategies in postsecondary education, particularly when combined
with instructional support. By integrating retrieval practice, spaced repetition, and elaborative
encoding, structured flashcard techniques can enhance memory retention and metacognitive
accuracy. The results emphasize the need for consistent instructional support and self-regulated
learning strategies to maximize their benefits. These findings contribute to the growing body of

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literature on evidence-based study strategies and highlight important considerations for educators
and learners when implementing flashcard-based learning techniques.

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4. Exploring the Efficacy of GenAI Tools in Post-Secondary Education
At all educational levels, the demand for students to comprehend, retain, and integrate
vast amounts of information often leads to significant test anxiety (Connon et al., 2016).
Common student behaviours consistent with assignment and test anxiety include procrastination,
ineffective study habits, distraction, cramming to complete an assignment or study for a test, and
mental “freezing,” to name a few (Trifoni & Shahini, 2011). Several studies have suggested that
test anxiety amongst university students can have deleterious impacts, including poor academic
performance, negative self-image, failing courses and dropout rates (Cassady & Johnson, 2002;
Gerwing et al., 2015; Hembree, 1988; Megivern et al., 2003). Nevertheless, in post-secondary
education institutions, where innovative technologies continually reshape research and
pedagogical approaches, the integration of generative artificial intelligence tools presents
avenues for enhancing student learning experiences.
Artificial intelligence (AI) and GenAI tools like chatbots have emerged as promising
avenues to improve learning outcomes related to critical thinking, writing proficiencies, and
study skills (Chiu, 2024). AI is the science of using computers, algorithms, and datasets to
achieve human-like problem-solving by analyzing a specific environment and acting toward a
defined goal (Sheik, 2023). GenAI is a set of technological tools that can produce new and
unique outputs, including text, images, computer coding, and videos, in response to input
commands (Chiu, 2024). Chatbots are a type of GenAI designed to simulate human conversation
or writing, with ChatGPT being a common example of such an application. ChatGPT, developed
by OpenAI, is based on a Generative Pre-trained Transformer architecture that uses deep
learning techniques to generate human-like text based on the input it receives (OpenAI, 2023).

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While previous research (e.g., seminal work by Beck, Stern, & Haugsjaa, 1996 and
Woolf, 1991; or a more recent systematic literature review on the topic by Tahiru, 2021) has
explored the application of AI within educational milieus, an examination of the potential for
chatbots to enhance student learning outcomes necessitates further empirical scrutiny. For
example, it is unclear if such learning aids ameliorate academic learning and test anxiety. This
chapter explored whether student learning and general test anxiety can be improved by
purposefully integrating GenAI tools into students’ approaches to course assignments.
Specifically, the research investigated how students engage with chatbots as ‘study confidantes’
to support content learning and how students use AI-generated essays to develop their academic
writing skills by critically evaluating their structure and content. As Chiu (2024) emphasized, it
is critical that students, the most important stakeholders in education, are involved in research
that explores the impact of GenAI.
The implications of this study serve to understand how educators’ learning outcomes can
be more easily achieved with well-designed assignments incorporating GenAI tools.
Understanding the efficacy of GenAI tools in a learning context will enable instructors to design
assignments and learning activities that maximize student engagement and learning outcomes.
Similar to Sana et al. (2020), this research examined efficacy in terms of optimizing learning.
The study also contributes to the growing body of literature on AI in education, providing
empirical data on the benefits and limitations of GenAI tools. As research continues to emerge,
post-secondary institutions will be better able to utilize this body of literature to inform policy
decisions regarding the integration of advanced technologies in the curriculum.

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Test Anxiety
In many post-secondary courses, students are tasked with comprehending, retaining, and
integrating substantial volumes of information across multiple courses each semester. This
situation, compounded by similar test and assignment due dates, creates a challenging overall
learning environment due to the need to synthesize extensive content that may lead to
pronounced test anxiety. Rana and Mahmood (2010; see also Gerwing et al., 2015) reported a
significant negative correlation between students’ achievement scores and their levels of test
anxiety, highlighting the salience of cognitive factors, particularly worry, over affective factors
such as emotional distress. Their investigation posited that test anxiety constitutes a contributing
element to students’ suboptimal performance and academic underachievement. Importantly, their
study emphasized the potential for mitigating test anxiety through targeted interventions aimed at
equipping students with coping mechanisms to address the underlying stressors. Instructors can
strive to mitigate some test anxiety by providing explicit assignment and test orientations and
preparation, encouraging positive thinking, seeking peer support, and recommending relaxation
training, to name a few interventions (Rana & Mahmood, 2010). Gerwing et al. (2015) further
concluded that educators should inform students of the prevalence of test anxiety as a potential
method to reduce the stigma of test anxiety.
Taken together, these suggestions related to students’ academic stress accentuate the
imperative for examining further interventions designed to bolster students’ capacity to achieve
the intended learning outcomes and ameliorate test anxiety. GenAI may offer some promise
through purposefully designed critical analysis assignments, self-instructional training, or the
preparation of exam notes to enhance study skills and thereby increase confidence and reduce the
negative impact of test anxiety.

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Cognitive Load, Active and Authentic Learning
Cognitive load theory, introduced by Sweller (1988), posits that learning is hindered
when cognitive resources are overloaded. This theory identifies three types of cognitive load:
intrinsic, extraneous, and germane (Sweller, 2010). Intrinsic load refers to the inherent difficulty
associated with a specific task; extraneous load is related to how information is presented; and
germane load pertains to the mental effort used to create schemas. Effective instructional design
aims to reduce extraneous load and optimize germane load, thereby facilitating efficient learning
processes.
Active learning, a core principle of constructivist theory, is also supported by GenAI
tools. Constructivist learning theories, championed by Piaget (1952) and Vygotsky (1978),
emphasize the active role of learners in constructing knowledge through interaction with their
environment. These theories advocate for learning environments that promote exploration,
problem-solving, and the application of knowledge in various contexts. Constructivist
approaches encourage learners to actively engage with the material, fostering deeper
understanding and retention.
Seeking new learning opportunities is important for all students to achieve the intended
learning outcomes, which is well-supported in educational research. Another concept, authentic
learning, emphasizes that students are more motivated and better prepared for success when their
education mirrors real-life contexts and addresses topics relevant to their lives outside of school.
This approach encourages students to actively seek out and engage in new learning experiences,
thereby enhancing their understanding and retention of knowledge (Lombardi & Oblinger, 2007;
Nachtigall et al., 2022).

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These educational approaches highlight the critical role of students proactively engaging
in new learning opportunities to achieve their intended educational goals. By fostering a mindset
of curiosity and continuous improvement, students not only enhance their academic performance
but also develop essential skills that prepare them for real-world problem-solving and lifelong
learning.
Educational use of GenAI
Instructors have been creatively adapting the use of GenAI to enhance critical thinking,
digital literacy, writing, and study skills (Kee et al., 2024; Muthmainna, 2022; Zawacki-Richter,
Marín et al., 2019) and potentially contribute to reducing academic anxiety. Chatbots can assist
students in learning a particular concept through the use of text prompts and conversational
responses. One of the chatbots that has received widespread attention from the academic field
due to both the opportunities it affords and concerns related to academic integrity is ChatGPT,
where GPT stands for “generative pre-trained transformer.” Language-processing chatbots
undergo pre-training on extensive language model databases, characterized by multiple stages.
Typically, the initial phase involves supervised learning, wherein a specific prompt is chosen
from a corpus, followed by the provision of a desired response by a human agent. Subsequently,
the neural network fine-tunes its outputs based on this input. In the subsequent phase, the
network is presented with a prompt, generating several responses that are subsequently assessed
for quality by human evaluators. These evaluations serve to enhance the reward model employed
by the network. Moreover, the model’s efficacy can be further refined by integrating “policies”
designed to impose constraints on specific types of outputs. For instance, given a prompt such as
“Describe Chomsky’s perspectives regarding ChatGPT,” the chatbot processes the input and
generates a written response.

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Researchers and practitioners are beginning to explore the potential of chatbots for
enhancing learning processes and reducing test anxiety. Empirical evidence suggests that
affording students self-testing opportunities, such as practice quizzing, can significantly enhance
their retention of course material while also fostering metacognitive awareness of their
comprehension levels (Stewart et al., 2014). This set of conclusions aligns with the constructivist
view that self-regulation and active engagement in learning activities are crucial for effective
learning (Zimmerman, 2002). The question arises: can GenAI tools, such as chatbots, serve as
facilitators for heightened learning retention and bolstered confidence in assignment
submissions, thereby mitigating academic anxiety?
Building upon the current research context, this study investigated two emerging lines of
inquiry: (1) How effective are GenAI tools in enhancing post-secondary students’ learning
outcomes? and (2) To what extent can GenAI tools mitigate test anxiety? The first question was
delimited by contextual factors encompassing the academic discipline and the course level.
Specifically, this research explored how first-year college and university students in psychology
could use a chatbot as a “study confidante” to assist with self-testing for difficult course
materials before a midterm. Furthermore, a secondary assignment was devised to afford students
an opportunity to learn the intricacies of crafting an academic essay. In this secondary task,
students selected a chatbot of their preference to generate an academic essay, subsequently
appraising the content and structure based on a rubric provided by the instructor for this
assignment in advance.
Based on emerging research disseminated at conferences and in scholarly journals (e.g.,
Li et al., 2021; Mon et al., 2023), this study posed two hypotheses. Hypothesis 1 posits that
students can elevate their learning and effectively attain specified course learning objectives

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through purposefully designed assignments integrating GenAI. In addition, empirical
investigations have revealed that test anxiety exerts a negligible influence on exam performance
when adjusted for preceding levels of knowledge (Theobald et al., 2022). Theobald et al. also
identified a correlation between lower levels of knowledge and heightened test anxiety.
Consequently, hypothesis 2 posits that implementing assignments incorporating GenAI to
augment students’ knowledge levels will concomitantly mitigate test anxiety levels. This study
includes students’ JOLs, which are metacognitive self-assessments of how well a person has
learned particular information (e.g., Koriat & Bjork, 2005; Koriat & Shitzer-Reichert, 2002; Son
& Metcalfe, 2005), to assess the efficacy of learning using assignments that integrate GenAI.
Method
The primary objectives of our investigation were to assess the efficacy of (1) self-testing
facilitated by chatbots in enhancing learning processes and, thus, potentially conferring cognitive
advantages to students and (2) using written material produced by chatbots to learn about the
structure of academic writing. In the context of this research, the efficacy of GenAI in learning
environments refers to students’ self-assessment of enhanced learning towards specific learning
outcomes and mitigated test anxiety. Efficacy is measured by the extent to which these tools
improve students’ self-assessment of improved learning outcomes such as comprehension,
retention, critical thinking, and writing skills. This study comprehensively examined the viability
of integrating chatbots within the instructional assignments for an Introduction to Psychology
course offered at UNBC and CNC during the Spring Intersession of 2023. Central to our inquiry
was the exploration of the extent to which chatbots could augment student learning outcomes and
foster deeper comprehension of course content. A mixed methods (i.e., qualitative and
quantitative) methodological approach was taken following Creswell and Creswell (2017).

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Research Site and Participants
This study was conducted at two post-secondary institutions in British Columbia,
Canada: the University of Northern British Columbia (UNBC) and the College of New
Caledonia (CNC). The sample consisted of 40 participants (12 male and 28 female; mean age =
23) enrolled in introductory psychology courses at these institutions. Of these, 14 participants
were native speakers of English, while the remaining participants spoke one of India’s official
languages or dialects.
At the UNBC, the study involved 18 participants who attended face-to-face courses. The
gender distribution at UNBC included 14 females and four males. Participants’ ages ranged from
18 to 30 years, with an average age of approximately 20.6 years (SD = 3.07). Table 5 details the
participants’ first and second languages by institution. The CNC study included 22 participants
who attended an online course. The gender distribution included 16 females and six males.
Participants’ ages ranged from 19 to 44 years, with an average age of approximately 22.5 years
(SD = 4.84).
Data Collection Procedures
Two assignments involving GenAI were incorporated into the course curriculum. These
assignments were developed to align with two specific learning outcomes. In the first
assignment, the students were required to ask a chatbot of their choice to explain a difficult
concept related to the course material. In this assignment, the students used GenAI as a “study
confidante” to help prepare for their midterm exam. This assignment was aligned with the
following learning outcome: students will be able to demonstrate a comprehensive understanding
of key psychological theories, concepts, and terminology across major domains of psychology
and to critically evaluate relevant information from different sources.

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In the second assignment, the students asked a chatbot to write an academic essay and
then graded it based on the instructor-provided marking rubric. This second assignment was
aligned with the following learning outcome: students will be able to critically evaluate writing
examples and will demonstrate proficiency in identifying and analyzing the coherence, clarity,
and organization of written arguments, as well as the effectiveness of evidence and support used
to bolster claims.
The assignment instructions provided to the students were as follows: for the first
assignment, students were tasked with engaging a chatbot of their choice (e.g., ChatGPT) to
explain a concept they found challenging while preparing for their midterm examination.
Emphasis was placed on carefully formulating questions/prompts, with students being
encouraged to refine their queries based on the chatbot’s responses. Subsequently, students were
required to document their interactions by taking a screenshot of the conversation and submitting
it as part of the assignment. The course instructor allocated participation marks for formulating
their prompt in a way that would allow the chatbot to generate a detailed and accurate response.
If the answer generated by the chatbot seemed insufficient or off-topic, the students were
required to ask follow-up questions. In their comments that constituted the second part of the
assignment, the students were asked to reflect on how the information provided by the chatbot
compared to the course textbook and/or the lecture material. In the subsequent class discussion,
the instructor selected a few examples of how the information generated by the chatbot appeared
to be incorrect or outdated and discussed with the class the importance of cross-referencing AIgenerated responses with peer-reviewed scientific research or, at minimum, with the textbook.
For the essay analysis assignment, students were tasked with providing instructions to a
chatbot of their choice to compose an essay on a topic covered in the course curriculum. Once

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again, students were told to craft their questions/prompts carefully and to refine their inquiries
based on the chatbot’s response. Utilizing a grading rubric supplied by the instructor, students
were tasked to evaluate the essay as if they were instructors assessing a student’s academic
submission. During a class session, the instructor explained the rubric’s categories and scale to
ensure students’ understanding. On the rubric, students were prompted to justify the mark they
assigned, reflecting on the purpose of the assignment and contemplating how the integration of
GenAI could be incorporated with traditional classroom assignments.
Additionally, students were encouraged to consider potential implications and caveats
associated with utilizing GenAI. Upon completion, students were required to submit both the
completed rubric with their comments and the essay generated by the chatbot. The goal of this
assignment was to encourage critical thinking by tasking the students with evaluating someone
else’s written work based on detailed and clear criteria contained in the rubric and familiarizing
students with the elements and the structure of an academic essay. The instructor reviewed each
submission for sufficient, precise, and well-worded instructions provided to the chatbot, as well
as the clarity of the assigned mark’s justification.
At the onset of the semester, students completed a pre-test measure using the Westside
Test Anxiety Scale (Driscoll, 2007). Following their final exam, students were briefed about the
study’s objectives and its association with two specific assignments. They were then presented
with a data release consent form, affording them the option to opt out of their data being utilized
for the study. Students who provided consent were administered either a paper-based survey (for
the in-person course at the university) or an online survey link (for the online course at the
college).

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The survey collected: (1) basic demographic information (e.g., age, sex, gender identity,
first language, multilingualism, and learning disorder history); (2) ratings of study strategies
used, including frequency of cramming, and self-reported usefulness of flashcards and AI-based
assignments; (3) perceived effectiveness of chatbot-assisted assignments for concept
understanding and academic writing; (4) likelihood of future use of flashcards and AI tools; (5)
open-ended responses on study strategies and course experience; and (6) a post-test
administration of the Westside Test Anxiety Scale. In addition, participants were asked to
provide a JOL by estimating their expected final course grade on a scale from 0 to 100%.
To ensure impartiality and minimize potential biases, survey data were collected
independently by a research assistant, distinct from the course instructor who served as the first
author. These data were securely housed in a designated laboratory until final grades were
submitted and approved for both courses.
Ethics Approvals
Research ethics approvals were obtained from both UNBC (approval number 6009020 –
Appendix 5) and CNC (approval number EO-2024-0508-102 – Appendix 3) in accordance with
the Panel on Research Ethics guidelines (2023). The students in both institutions were informed
about the study after their final exam and provided with a data release consent form with the
possibility to opt out of their data being used for the study. Students who gave their consent were
provided with a paper-based survey in UNBC and an identical web-based survey in CNC that
assessed their study habits and post-test anxiety levels using the Westside Test Anxiety Scale.
To ensure that participation did not influence grading outcomes, data analysis was
postponed until two weeks after students’ final grades had been submitted. Confidentiality and
anonymity have been maintained for all participants by anonymizing all responses after the

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student grades were associated with survey responses. Data confidentiality was strictly
maintained in accordance with institutional and ethical guidelines. Identifiable information was
not collected, and all data were stored securely.
Additionally, to mitigate any potential conflict of interest arising from the Principal
Investigator’s dual role as both researcher and course instructor, it was research assistants who
distributed and collected the research surveys following the final exam. These safeguards
ensured the researcher’s non-involvement in grading and data collection processes, thereby
maintaining the integrity and impartiality of the research outcomes.
Data Analysis
Data from surveys and assignments were entered into a single .csv file and imported into
R for preprocessing, analysis, and visualization. Descriptive analytics were used to identify
trends and relationships in the survey response data. A dependent measures t-test examined any
differences between pre- and post-test anxiety scores. Given the similarities between the
response profiles of both the university and the college students, the analysis collapsed the data
and provided descriptive analytics on the aggregate data.
Spearman’s rank-order correlation was conducted to evaluate the relationship between
students’ expectations (i.e., JOLs) and their actual performance. JOLs refer to individuals’
predictions of their future performance on a task based on their current understanding or
knowledge (Koriat & Bjork, 2005; Son & Metcalfe, 2005). Additionally, correlations between
post-test anxiety scores and both expected and actual final grades were assessed. This analysis
examined whether students’ self-reported anxiety levels at the end of the course were related to
their expected grades (i.e., JOLs) and their actual academic performance.

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To ensure the validity and reliability of the findings, data screening procedures were
implemented to check for missing values, outliers, and assumptions of normality. The use of
Spearman’s rank-order correlation was particularly beneficial in this context as it is less sensitive
to outliers compared to Pearson’s correlation and is suitable for data that do not necessarily
follow a normal distribution.
Results
Efficacy of Learning
The post-course survey findings reveal positive perceptions of chatbot assistance in
understanding course material and constructing academic papers. The majority of students (86%)
stated that using a chatbot as a “study confidante” helped them better understand/remember
specific course information for an exam. For the essay grading assignment, 61% of university
students expressed that the process of reviewing and analyzing an AI-generated essay helped
them understand the structure of an academic essay, and 83% of college students reported that
the essay grading assignment helped them understand the information better. A small minority
(12.5%) expressed that they disagreed that this assignment helped them understand the structure
of an academic essay. It was noted that 60% of the students who disagreed that the assignment
helped them understand the structure of an academic essay (i.e., 3 out of 5 students) had English
as their first language (EFL). In contrast, only 4% of the total number of non-English as a first
language (non-EFL) students expressed disagreement. Prior experience with essay writing at the
post-secondary level may explain the difference between EFL and non-EFL students.
The post-course survey also explored students’ likelihood of using GenAI tools like
chatbots in future classes. Most students (70%) stated that they would likely use chatbots in
future classes. Based on the feedback provided by the students during the class discussions, the

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intended learning outcome for the first GenAI assignment was achieved: in addition to major
psychological concepts, the students understood that as helpful as the AI-generated information
may be, it is not always reliable and, therefore, necessitates careful scrutiny. It is also worth
noting that ChatGPT was the most common, but not the only GenAI tool that students chose to
use for the two assignments. Some other chatbots of choice were Tiimo Vercel, My AI,
essaywriters.ai, and Microsoft Bing.
Qualitatively, some of the feedback provided by the students within their submissions
included the following comment:
I feel that Al can be incorporated into traditional classrooms as it is a highly useful source
of information, that is available at any time with access to all the information you would
ever need to know. I would say that students using Al need to be made aware that they
must do a bit of fact-checking with some of the information before assuming it is all
correct. As well as ask the Al a question a few different ways before just going off the
first bit of info they receive to make sure it is true and all answers they are getting are
relatively the same.
Other comments included: “Although there was a level of understanding on the topics,
using information without proper sources is plagiarism” (the student assigned the “F” grade to
the essay generated by the chatbot); and “The essay shows a partial understanding of the
material, but lacks depth and thoroughness in discussing sensation and perception.” In the
subsequent class discussion, the instructor selected some examples of fake references generated
by chatbots (a commonly known limitation) to emphasize the importance of information crosschecking once again and sought students’ feedback on the ethical considerations of incorporating
GenAI into contemporary post-secondary education. The students expressed the importance of

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having clear guidelines to follow on the institution’s and instructors’ part regarding the
acceptable use of GenAI technology and the transparency on the student’s part when it is being
used. Some students shared that it is often unclear whether instructors find using such tools
acceptable to use, which leads to either fear of utilizing any AI tools or withholding the fact
when they are being used. This feedback underscores the need for thorough examination when
using AI tools for academic purposes.
Examining Test Anxiety and JOLs
The students’ average anxiety score remained relatively stable from the pre-test to the
post-test at “high normal test anxiety” (Driscoll, 2007). The average pre-test anxiety score was
2.82, which corresponds to high normal test anxiety, while the average post-test anxiety score
was 2.97, which was still at high normal test anxiety. A dependent samples t-test illustrated no
difference between pre- and post-test anxiety scores, t(35) = 0.36, SEM = 0.203, p > 0.72,
indicating that the difference between the pre-test and post-test anxiety scores is not statistically
significant at conventional significance levels. The four students who did not complete the pretest anxiety measure at the beginning of the semester were excluded from this analysis.
In addition to analyzing survey data concerning student perceptions of GenAI
assignments, this study examined JOLs (Koriat & Bjork, 2005; Son & Metcalfe, 2005) by
assessing students’ self-reported expected final course grades and their correlation with the
actual final course marks they received. The students’ mean anticipated grades, that is, their JOL
for the course (𝑥 = 78.65), exceeded their actual final grades (𝑥 = 64.37) by approximately 14
percentage points. This disparity suggests a tendency towards inflated JOLs, which may have
arisen due to a fundamental difference between learning and testing conditions (Koriat & Bjork,
2005; Koriat & Shitzer-Reichert, 2002). In addition, Spearman’s rank-order correlation was used

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to evaluate the relationship between students’ expectations and their actual performance. The
statistical analysis yielded a moderate, positive correlation (rs = 0.413, p < .05), indicating that
students with higher expectations tended to achieve higher grades than their peers. However, the
moderate correlation underscores the limited predictability of actual performance based solely on
students' expectations. Despite some alignment between expectations and academic outcomes,
the correlation suggests that other factors also influence students’ final grades.
Spearman’s correlation was also conducted to assess the relationship between the posttest Westside Test Anxiety scores and the self-reported expected final grades. The correlation
coefficient was 0.016, p = 0.92, indicating no significant relationship between the two variables.
Finally, Spearman’s correlation was performed between the post-test Westside Test Anxiety
scale scores and the actual final grades, revealing no significant relationship between anxiety
levels and final grades, rs = –0.103, p = 0.53, which is consistent with past research (Connon et
al., 2016).
Discussion
This study has demonstrated the potential of GenAI tools in enhancing learning
experiences in post-secondary settings. The integration of chatbots as “study confidantes” and
essay writers was perceived by students to positively influence comprehension and retention of
course material. While the anticipated reduction in test anxiety was not observed, the findings
underscore the potential of chatbots in improving learners’ perceived understanding of writing an
academic essay. The present research contributes to the scholarship of teaching and learning by
examining the utility and effectiveness of GenAI tools in supporting student learning outcomes
and aiding their test anxiety. The stability of test anxiety scores suggests that further

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investigation is needed to explore the relationship between test anxiety and learning
interventions.
Overall, the results provided partial support for the hypotheses. Hypothesis 1, which
posited that students could elevate their learning and effectively attain specified course learning
objectives through purposefully designed assignments integrating GenAI, was supported by the
positive perceptions and improved understanding reported by students. However, hypothesis 2,
which proposed that the implementation of assignments incorporating GenAI would mitigate test
anxiety levels, was not supported as the difference between pre-test and post-test anxiety scores
was not statistically significant.
Even though the two assignments involving GenAI, as designed and implemented in the
two courses, did not lead to a statistically significant reduction in test anxiety among participants,
the present descriptive research provides a valuable exploration of students’ perspectives
regarding the integration of chatbots as learning aids and the potential ramifications for future
teaching and learning methodologies. As underscored by Chiu (2024), pedagogical inquiries
aimed at integrating technology to enhance learning outcomes necessitate consideration of
student perspectives. Our study engaged students in self-reflection regarding their experiences
with GenAI-enabled learning activities, encompassing tasks to demonstrate a comprehensive
understanding of key psychological concepts and critically evaluate writing examples. Overall,
the findings indicate that students held positive perceptions of assignments incorporating GenAI,
noting its beneficial impact on their learning endeavours. As Ertmer and Newby (2013) remark
while reflecting on their seminal work from 20 years ago, the widespread availability of the
Internet and Web 2.0 tools, the emergence of a new generation of learners with distinct
preferences and capabilities, and the evolution of teaching methods that predominantly align

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with the tenets of constructivism are the three major forces shaping today’s learning processes.
Ertmer and Newby (2013) stress the imperative of adapting instructional design to meet the
evolving needs of today’s learners, advocating for contextualized, personalized, and
collaborative learning experiences. These findings hold significant implications for educators
seeking innovative strategies to enrich student learning outcomes.
Building upon Ertmer and Newby’s (2013) contention that instructional designers should
prioritize the efficacy of learning theories in facilitating mastery of specific skills rather than
adhering to a one-size-fits-all approach, this principle extrapolates to the realm of individual
assignments. This extension prompts us to explore how assignments can be meticulously crafted
to foster targeted learning outcomes tailored to the unique needs of learners. In the ever-evolving
landscape of education, characterized by the advent of new and innovative technologies, the
integration of GenAI tools emerges as a promising avenue for enhancing student learning
experiences. Instructors can create dynamic learning environments that resonate with
contemporary learners by intentionally designing learning assessments that leverage GenAI to
align with course objectives. Moreover, soliciting ongoing formative feedback from students
regarding the efficacy and relevance of these assignments enables educators to fine-tune
assignments for future use, fostering a culture of continuous improvement in pedagogical
practices within higher education.
Students’ positive reception of chatbots points towards a promising direction for
integrating AI in post-secondary education. These findings align with the hypothesis that GenAI
tools can serve as effective pedagogical aids. However, the lack of a significant decrease in test
anxiety from pre- to post-test highlights the complexity of test anxiety as an affective state,
which may not be easily ameliorated through academic assignments. The discrepancy between

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students’ expected and actual grades also reveals an overconfidence bias, which GenAI tools did
not affect. These results suggest that while GenAI can enhance content comprehension and
academic skills, its role in fostering realistic self-assessment and mitigating psychological
aspects of learning demands further exploration.
Purposefully designed assignments using GenAI chatbots can minimize the extraneous
cognitive load (Sweller, 2010) by providing information in a clear, concise manner and allowing
the opportunity for immediate revisions based on the prompts generated by the students
themselves. This immediate feedback helps prevent learners from becoming overwhelmed by
irrelevant or inadequately structured information. A lack of prompt feedback from the instructor
was found to cause confusion, anxiety and frustration in students (Hara, 2000). By offering
scaffolding and adaptive support, chatbots can enhance germane cognitive load, encouraging
learners to invest mental effort in meaningful learning activities, such as critical thinking (e.g.,
evaluating whether the generated response is appropriate) and problem-solving.
GenAI assignments can also facilitate active learning by engaging students in interactive
dialogues, posing questions, prompting reflections, and providing opportunities for selfassessment. These interactions can stimulate learners’ cognitive processes, encouraging them to
actively apply and consolidate their knowledge. For example, a chatbot might simulate a realworld scenario related to the course content, requiring students to apply their understanding in a
practical context. The benefits of active learning were found to lead to increases in examination
performance and decreases in failure rates compared with traditional lectures (Freeman et al.,
2014).
Therefore, the integration of GenAI tools, such as chatbots, can support cognitive load
theory by optimizing the balance of intrinsic, extraneous, and germane cognitive load and align

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with constructivist principles by fostering active and personalized learning experiences. By
leveraging these theoretical frameworks, GenAI tools can enhance the effectiveness of
instructional design, ultimately improving student learning outcomes.
Limitations of the Study
Although the two assignments involving GenAI as designed and implemented in the two
courses did not lead to a statistically significant reduction in test anxiety among participants, the
present descriptive research provides a valuable exploration of students’ perspectives regarding
the integration of chatbots as learning aids and the potential ramifications for future teaching and
learning methodologies. The sample size was relatively small and limited to two smaller
institutions, which may not be representative of broader post-secondary student populations.
Additionally, the reliance on self-reported data may introduce bias, as students’ perceptions and
reported behaviours may not fully align with their actual experiences and outcomes. However,
this diverse group of participants provided a broad range of perspectives and experiences among
post-secondary students, enriching the study’s findings. The linguistic and cultural diversity
within the sample is an important consideration in understanding the varied impacts and
perceptions of GenAI tools in today’s post-secondary educational settings.
Future Directions
Future research should focus on developing a more nuanced understanding of how
different types of GenAI tools can be optimized for various learning contexts and student needs.
Additionally, exploring the integration of GenAI tools with other interventions to reduce test
anxiety could offer a more comprehensive approach to improving educational outcomes. As this
study has shown, GenAI holds considerable promise for enhancing post-secondary education,
but its full potential needs continued investigation and innovative pedagogical practices.

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Finally, in the context of integrating GenAI tools within academic settings, it is
imperative to consider the reflections and feedback provided by students. As observed in the two
courses where GenAI tools were employed for assignments, students exhibited a significant level
of maturity and critical thinking. In their written feedback and during class discussions that
followed the assignments, students articulated insightful views concerning the limitations of
GenAI tools, particularly the issues related to accuracy and ethical considerations. This
engagement indicates a deeper understanding and a critical perspective on the role and
implications of GenAI in academic environments.
The articulation of concerns and perspectives by students regarding GenAI underscores
the necessity for their active participation in dialogues surrounding institutional policies and the
future role of GenAI in academia. It is paramount that educational institutions recognize the
value of incorporating student voices into these discussions. Including students in the
conversation ensures that policies and practices surrounding the use of GenAI in academic
settings reflect not only technological and pedagogical considerations but also the ethical and
practical concerns of those directly impacted by these technologies.
The active involvement of students in evaluating GenAI tools within academia is
essential for fostering critical thinking and ethical reasoning. Discussion should extend beyond
technological capabilities to include ethical considerations such as academic integrity and the
impact of AI on learning. Among the various concerns related to post-secondary students’ use of
GenAI, ethical issues are most prominent (Mahmud, 2024; Luo, 2024). However, as Eaton
(2023) argues, in the era when AI and neurotechnology are integrated into daily life, ethics and
integrity become crucial as these technologies become inseparable from everyday life, impacting
how we teach, learn, and interact. The ineffectiveness of trying to police the use of cutting-edge

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technologies for academic honesty suggests the need for transdisciplinary research to understand
the ethical implications of such technologies in education. The approach the present work
advocates for advances a technologically proficient learning environment and promotes ethical
responsibility and intellectual development (Reich & Ito, 2017).
Conclusions
Incorporating student perspectives and student-informed research into the discourse on
GenAI reflects democratic principles in educational policymaking, ensuring decisions are
informed by the stakeholders most affected by them — students themselves (Biesta, 2015; Chiu,
2024). This participatory process may allow for GenAI’s integration into curricula in a manner
that is both educationally effective and ethically sound, supporting broader educational
objectives of critical engagement and informed citizenship. In conclusion, GenAI allows us to
creatively adapt the instructional design and well-articulated learning outcomes (e.g., Sana et al.,
2020) to provide today’s learners with contextualized, personalized, and collaborative learning
experiences.

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5. Conclusion: Advancing Learning Through Cognitive Research, Established Practices,
and AI Tools
How do we optimize learning in an era of rapid technological advancement while
maintaining a foundation in cognitive science and established educational strategies? The
dissertation addressed this overarching question by integrating insights from cognitive
mechanisms, retrieval-based learning, and AI-driven tools to enhance knowledge acquisition and
application. Through three distinct yet interconnected chapters, this research examined singleword processing, explored the efficacy of structured learning techniques, and evaluated the role
of GenAI in higher education. Together, these studies contribute to the evolving discourse on
cognitive mechanisms of learning, effective teaching methodologies, and their implications for
the SoTL.
Chapter 2 investigated the cognitive underpinnings of single-word processing,
emphasizing the role of orthographic and phonological mechanisms in reading and language
acquisition. Building on the theoretical frameworks outlined in the introduction, particularly
Stanovich’s (1980) interactive-compensatory model and the Reading Systems Framework
(Perfetti & Helder, 2022), this chapter explored how readers recognize words and extract
meaning from text, even in the presence of early sensory disruptions.
Word recognition is a highly interactive and adaptive process, involving the automatic
activation of phonological, orthographic, and semantic representations, even during silent
reading (Seidenberg et al., 2022). Theories such as the dual-route model and connectionist
frameworks offer distinct perspectives on how reading occurs. The dual-route model posits that
skilled readers can recognize words through either a lexical route (direct word recognition) or a
phonological route (sublexical decoding based on sound-symbol correspondence) (Coltheart,
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2006). In contrast, connectionist models propose that reading is a probabilistic, distributed
process, where word recognition emerges from repeated exposure to linguistic input,
continuously strengthening the links between orthographic patterns, phonology, and semantics
(Seidenberg et al., 2022).
These frameworks align with Stanovich’s (1980) view that weaknesses in one component
of the reading system may be compensated for by strengths in another, such that individuals with
reduced phonological input may increasingly rely on orthographic or semantic information. The
findings of this study reinforce the importance of phonological activation in visual word
recognition, supporting both dual-route and connectionist models. Masked priming studies have
shown that phonological information is accessed automatically, even when reading tasks do not
require overt pronunciation, challenging traditional assumptions that phonology plays a postlexical role. Instead, phonological activation occurs in parallel with orthographic and semantic
processing, dynamically shaping how words are recognized and understood (Perfetti & Helder,
2022).
A critical component of the research in Chapter 2 was evaluating whether early auditory
disruptions, such as recurrent otitis media, have lasting effects on phonological processing.
Given that phonological awareness is foundational to reading development (Carroll &
Breadmore, 2018), previous research has suggested that children who experience fluctuating
hearing loss due to otitis media may struggle with phoneme discrimination, potentially leading to
long-term difficulties in reading (Winskel, 2006; Mody et al., 1999). However, the results of this
study demonstrated no significant differences in phonological processing between young adults
with a history of recurrent otitis media and those without. While phonological congruency had a
clear impact on word recognition accuracy, reinforcing its role in reading, the absence of

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significant between-group differences suggests that compensatory mechanisms may have
enabled affected individuals to achieve normative reading proficiency over time (Stanovich,
1980).
This finding is particularly relevant in light of Stanovich’s (2009) Matthew Effect, which
suggests that early phonological deficits can lead to cumulative academic disadvantages when
unaddressed. The current results, however, imply that not all early disadvantages result in longterm impairment, and that cognitive plasticity or enriched literacy environments may support
recovery or adaptation.
Methodological factors and environmental conditions may have influenced the results, as
differences in participant demographics, stimulus presentation, and testing environments have
been shown to affect phonological processing outcomes. For instance, previous studies, such as
Owen and Borowsky (2003), reported a quadratic function in their results, while this study did
not replicate that specific pattern. Discrepancies in stimulus conditions, lighting environments, or
other procedural variations may have contributed to response differences, highlighting the
complexity of phonological-orthographic interactions in reading research.
Despite these variations, this study supported prior findings that phonological activation
plays a critical role in word recognition. Phonological awareness remains central to skilled
reading, reinforcing its significance in cognitive models of literacy. Furthermore, the results
highlighted potential applications beyond basic reading research. For instance, expertise in
phoneme-to-grapheme mappings, such as that developed by stenographers and interpreters, may
influence phonological-orthographic interactions, warranting further research into specialized
populations. Such populations merit further investigation, as prior studies on cross-modal
interactions suggest that phonological processing can be influenced by visual and even tactile

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inputs (McGurk & MacDonald, 1976; Millar, 2003), raising questions about how different
sensory modalities contribute to word recognition.
Given these findings, several avenues for future research emerge. Longitudinal studies
tracking individuals from childhood through adulthood could clarify whether phonological
processing deficits from early auditory disruptions fade naturally or are actively compensated for
through learning and literacy exposure. Additionally, neuroimaging research using fMRI or
ERPs could explore whether individuals with a history of otitis media exhibit different neural
activation patterns during reading tasks, shedding light on potential adaptive mechanisms in the
brain.
Aside from implications for phonological processing, the research in Chapter 2 informs
the development of AI-driven literacy tools that support reading acquisition. Since phonological
activation is integral to word recognition, AI-based reading interventions should incorporate
phoneme-grapheme mapping exercises and adaptive feedback mechanisms to reinforce
phonological awareness. By integrating insights from cognitive psychology, AI-driven reading
support can be tailored to individuals with phonological processing difficulties, helping optimize
learning experiences.
Therefore, this study reinforces the essential role of phonological and orthographic
interactions in word recognition, supporting theoretical frameworks such as the dual-route and
connectionist models. While recurrent otitis media has been associated with phonological
processing challenges in earlier research, the present findings did not reveal significant
differences in phonological discrimination among young adults with or without a childhood
history of otitis media. This finding suggests that, in the current sample, any early phonological
deficits may have been mitigated over time, potentially through compensatory mechanisms or

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enriched literacy experiences — an interpretation consistent with Stanovich’s (1980) interactivecompensatory model. Although these findings do not support a persistent deficit, they contribute
to a more nuanced understanding of developmental plasticity and its role in reading outcomes.
As such, they carry tentative implications for literacy interventions and technology-based reading
support, particularly for learners with histories of early auditory disruptions.
Beyond word recognition, effective learning depends not only on cognitive mechanisms
but also on the strategies learners employ to reinforce and retain information. Just as
phonological and orthographic processing interact dynamically to support reading fluency,
structured study techniques such as retrieval practice and spaced repetition leverage similar
cognitive principles to enhance knowledge retention and metacognition. The ability to retrieve
information, reinforce conceptual connections, and regulate study behaviours plays a critical role
in academic success.
As such, Chapter 3 shifted focus to applied educational strategies, examining the impact
of retrieval-based learning (via Quizlet), spaced repetition (via Cram), and elaborative encoding
(via Flashcards-Plus) on student performance. Through a series of experimental studies, this
research explored how different flashcard-based learning strategies, both digital and traditional,
affect knowledge retention, metacognitive accuracy, and self-regulated learning. The findings
suggest that active engagement in learning, while not always leading to immediate performance
gains, contributes to improved metacognitive calibration and student confidence.
Retrieval practice and spaced practice are two foundational learning techniques that
enhance retention and knowledge transfer (Dunlosky et al., 2013). Active recall, a key
component of retrieval practice, strengthens neural pathways associated with memory, leading to
long-term retention and improved application of knowledge. The study described in Chapter 3

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confirmed that flashcards inherently promote retrieval practice, particularly when used for selftesting, fostering deeper cognitive engagement. Experiment 1 compared retrieval practice
through Quizlet flashcards and spaced repetition via Cram flashcards. While both methods were
effective, no significant performance difference was observed between the Quizlet and Cram
groups, but students who used flashcards consistently outperformed non-users on the final exam,
suggesting that these techniques particularly benefit long-term retention rather than short-term
exam performance.
Students often underestimate the effectiveness of retrieval-based learning, favouring
passive study techniques such as rereading and highlighting. This misjudgment is linked to
inaccurate JOLs, where students overestimate their comprehension and retention abilities
(Koriat, 1997; Dunlosky et al., 2013). Experiment 2 revealed that students who engaged in
retrieval practice exhibited better calibration in their self-assessments, improving their ability to
allocate study time effectively. However, test scores remained stable across different study
methods, indicating that additional instructional support may be necessary to help students
sustain effective study habits and translate metacognitive gains into performance improvements.
A key innovation explored in this study was the Flashcards-Plus strategy (Appleby, 2013;
Senzaki et al., 2017), which integrates elaborative encoding by requiring students to paraphrase
definitions and generate personalized examples. Unlike traditional flashcards that primarily
support rote memorization, Flashcards-Plus encourages deeper cognitive processing by linking
new information to prior knowledge. Although this study did not include a control group using
traditional flashcards, findings suggest a potential link between elaborative encoding and
improved metacognitive accuracy. Specifically, students using the Flashcards-Plus method
demonstrated a strong correlation between their expected and actual performance, even though

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overall test scores did not significantly differ across groups. These results are consistent with the
hypothesis that deeper encoding strategies may support more accurate self-assessment, but
further controlled comparisons are needed to draw firm conclusions about causality.
The study examined how flashcard-based strategies function across digital and paperbased modalities. While platforms such as Quizlet and Cram incorporate features like spaced
repetition and adaptive learning, the research did not find significant performance differences
between them. Experiment 2 used traditional paper-based flashcards and incorporated the
Flashcards-Plus strategy to promote elaborative encoding, though performance remained stable
across study phases. Although the study did not systematically assess student preferences, the
lack of significant performance advantage for paper or digital formats suggests that contextual
factors, such as study environment, consistency, and instructional support, may influence
learning outcomes more than modality alone.
Beyond improving retention, structured flashcard strategies also played a role in reducing
test anxiety and enhancing students’ confidence. Flashcards were found to provide a structured
approach to studying, helping students feel more prepared and reducing last-minute cramming
behaviours. However, despite students reporting decreased anxiety levels after using flashcards,
the research found no statistically significant correlation between test anxiety and final course
grades. This suggests that while structured study techniques can mitigate stress, additional
factors, such as metacognitive awareness, prior knowledge, and exam-taking strategies, may
influence overall academic performance.
Despite the effectiveness of retrieval practice and spaced repetition, a significant
challenge remains: ensuring students adopt and maintain these strategies over time. The study
revealed that while students initially engaged with flashcards effectively, their final exam

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performance declined, suggesting possible inconsistencies in strategy use during the latter part of
the course. However, because the study did not track study methods during the final exam
period, it is unclear whether students reverted to passive strategies or continued to use active
retrieval ineffectively. Moreover, students’ perceived usefulness of flashcards was not
significantly associated with their exam performance, highlighting a disconnect between strategy
preference and actual effectiveness. These findings point to the importance of instructional
interventions that not only introduce evidence-based techniques but also support sustained,
effective implementation throughout the academic term.
Finally, the findings of Chapter 3 research have broader implications for the design of
AI-powered study tools. As GenAI becomes increasingly integrated into education, there is a
growing opportunity to develop AI-enhanced flashcards that incorporate retrieval-based learning,
spaced repetition, and elaborative encoding. Adaptive learning technologies that personalize
study schedules based on students’ individual performance could further optimize retention and
engagement. However, ensuring that students actively interact with AI-generated content, rather
than passively relying on automated responses, remains a critical challenge. Future research
should examine how AI-driven flashcard tools can be designed to promote active learning,
improve metacognitive accuracy, and reduce cognitive overload while maintaining ethical
considerations in their implementation.
Together, these findings emphasize the importance of structured, evidence-based study
strategies in higher education. By combining retrieval practice, spaced repetition, and elaborative
encoding, flashcard-based learning methods offer an effective framework for enhancing
retention, metacognitive awareness, and academic performance. However, to maximize their
impact, educators must provide ongoing guidance, helping students develop self-regulated

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learning habits that extend beyond short-term exam preparation. Additionally, the format through
which these strategies are delivered may play a subtle but meaningful role in learning efficacy.
As discussed in earlier sections, embodied cognition frameworks suggest that paper-based
flashcards may reinforce memory by engaging sensorimotor pathways (Wilson, 2002),
highlighting the potential value of tactile interaction in reducing cognitive load and supporting
deeper conceptual understanding. Future research should further explore how modality
influences the effectiveness of structured study strategies in real-world contexts. This research
underscores the need for continued investigation into optimizing digital and traditional learning
tools, ensuring that students have access to effective, engaging, and adaptable study strategies
that support long-term knowledge acquisition and academic success.
As educational landscapes continue to evolve, integrating technology into learning
environments presents new opportunities and challenges. While retrieval-based study strategies
and structured learning methods have long been established as effective, the increasing
prevalence of AI in education introduces a paradigm shift in how students engage with learning
materials. AI-powered tools have the potential to personalize instruction, enhance cognitive
engagement, and streamline study processes, but they also raise important questions about
academic integrity, student autonomy, and the depth of learning.
Chapter 4 explored the integration of GenAI in education, assessing its potential to
support personalized learning, critical thinking, and academic writing. Student feedback
indicated that AI tools were generally well-received, particularly in enhancing comprehension
and essay-writing skills. Many students viewed AI chatbots as valuable study aids but also
recognized the risks of misinformation, emphasizing the need for content verification. While
most expressed interest in using AI in future coursework, uncertainty surrounding institutional

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guidelines led to hesitation or undisclosed AI use, highlighting the importance of clear policies
on responsible AI integration.
Despite AI’s ability to support learning, its impact on test anxiety was negligible.
Students’ JOLs tended to be inflated, with expected grades often exceeding actual performance.
This suggests that AI-assisted study tools can create an illusion of understanding, reinforcing
surface-level familiarity rather than deep mastery of concepts. To maximize AI’s potential while
mitigating its limitations, structured assignments should pair AI feedback with active
engagement, critical analysis, and source verification. For example, the essay evaluation task
required students to critique AI-generated essays using a rubric, which helped them learn
academic essay structure but also recognize concerns about plagiarism, fabricated references,
and AI’s lack of depth, reinforced the need for human oversight.
By bridging traditional and AI-enhanced learning methods, this dissertation underscores
the benefits of a holistic approach to education that integrates cognitive science, evidence-based
study techniques, and technological advancements. This integrative perspective acknowledges
that effective learning is not the product of a single method or tool but instead emerges from the
interplay between foundational cognitive-based-base principles (such as retrieval practice and
elaborative encoding), learner agency and metacognition, and adaptive technologies that
personalize and scaffold the learning experience. In doing so, this research highlights the
importance of designing educational environments that are both scientifically grounded and
responsive to the evolving digital landscape, ensuring that students are equipped not only to
acquire knowledge but also to transfer, apply, and regulate that knowledge in dynamic, realworld contexts.

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AI should ultimately be harnessed as a cognitive augmentation tool rather than a
replacement for human intellectual effort (De Cremer & Kasparov, 2021). When GenAI
applications are aligned with evidence-based learning principles, they can support the
development of engaging, effective, and ethically responsible curricula that enhance student
learning while preserving academic integrity (Reich & Ito, 2017). Rather than supplanting
human cognition, AI has the potential to empower students by fostering critical thinking, selfregulation, and strategic study habits, leading to deeper and more meaningful engagement with
knowledge (Chiu, 2024; Zawacki-Richter et al., 2019).
While AI presents opportunities for personalized instruction, it also raises critical
questions about ethical AI use and the preservation of deep learning. Future research should
investigate the long-term effects of AI-driven study tools on knowledge retention, student
autonomy, and academic integrity. This study found that while GenAI tools helped students
comprehend and remember content, they did not significantly reduce test anxiety or improve
metacognitive accuracy, indicating that such tools must be embedded within broader
interventions to support psychological outcomes (Theobald et al., 2022).
To ensure responsible AI engagement, institutions must develop comprehensive best
practices that encourage students to use AI as a tool for cognitive augmentation rather than a
substitute for intellectual effort. This involves not only clarifying policies around acceptable AI
use but also embedding AI literacy into curricula across disciplines. The assignments examined
in this study emphasized careful prompt formulation, source verification, and reflection, which
were perceived by students as helpful in fostering deeper engagement and ethical reasoning
around AI use (Chiu, 2024).

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By embedding AI literacy within broader digital literacy and academic integrity
frameworks, educators can empower students to use AI tools thoughtfully and transparently. This
also includes teaching students about the limitations of current AI systems, such as biases,
hallucinations, and a lack of contextual understanding. Promoting ethical AI use requires
creating a culture of reflection and accountability, where students view AI not as a shortcut, but
as a partner in their cognitive development.
Moreover, the design of AI-enhanced assignments should consider cognitive load theory
(Sweller, 2010), ensuring that extraneous load is minimized and germane load is optimized.
Chatbots, when used as interactive "study confidantes," provided just-in-time explanations,
allowed for follow-up clarification, and enabled active content revision — all of which helped
reduce confusion and cognitive overload (Chiu, 2024). These practices align with constructivist
learning theory, which emphasizes active engagement, problem-solving, and authentic learning
experiences (Vygotsky, 1978; Piaget, 1952).
Ultimately, fostering metacognitive awareness and ethical engagement with AI can help
institutions create learning environments where technology serves as an adaptive support system,
enhancing student learning while preserving academic integrity and intellectual rigour. This
includes acknowledging that while GenAI may aid performance on specific tasks, it does not
replace the need for effortful learning or personal accountability. Students in the study
recognized this nuance, often emphasizing the need to cross-check AI responses and to use the
tools critically and reflectively (Chiu, 2024).
Overall, this study underscores the potential of GenAI as a cognitive tool that can
enhance learning, but only when used critically and with clear instructional guidance. While AI
chatbots were perceived as helpful in improving comprehension and writing skills, their impact

128

on test anxiety was negligible, and they did not improve students’ accuracy in judging their own
learning. These findings highlight the importance of structured AI integration, where AI tools
support — but do not replace — critical thinking, self-assessment, and academic integrity.
The discrepancy between students’ anticipated and actual grades reflects overconfidence
bias, a common metacognitive issue not resolved by AI integration (Koriat & Bjork, 2005). This
gap reinforces the need for scaffolding tools that promote self-testing, spaced practice, and
reflective self-evaluation.
Moving forward, educators must carefully design AI-based assignments that encourage
active learning, reinforce self-regulation, and align with ethical academic practices. By fostering
AI literacy and clear institutional guidelines, higher education can harness AI’s strengths while
safeguarding against its potential pitfalls, ensuring that AI serves as a tool for cognitive
enhancement rather than a shortcut to learning. The integration of AI into education represents
just one dimension of a broader effort to optimize learning outcomes. Understanding how
students acquire, retain, and apply knowledge requires a multifaceted approach that draws from
cognitive science, traditional learning techniques, and technological innovations.
Together, the three chapters showcase a range of research methodologies, from cognitive
experiments to applied educational interventions and technology-driven innovations. This
dissertation underscores the value of using diverse methodological approaches to address the
multifaceted nature of learning and education. By examining how learners recognize and process
language, engage with structured study techniques, and interact with GenAI in academic
contexts, this research provides a broad view of the cognitive, behavioural, and technological
factors that shape educational outcomes.

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This dissertation emphasizes the importance of interdisciplinary research in optimizing
educational strategies by combining insights from cognitive psychology, instructional design,
and AI-enhanced learning environments. It demonstrates that meaningful learning occurs at the
intersection of evidence-based practices and adaptable tools, and that addressing complex
educational challenges requires both theoretical depth and practical application. Across its three
chapters, this work advocates for a multifaceted approach to learning that acknowledges
individual differences, supports learner autonomy, and leverages emerging technologies without
losing sight of foundational cognitive principles.
Looking ahead, the findings presented here suggest several promising directions for
future research. Continued investigation is needed into how cognitive mechanisms interact with
technological tools across different learner populations, including those with diverse linguistic,
cognitive, and accessibility needs. Longitudinal studies could further explore how sustained
engagement with strategies like elaborative encoding or adaptive AI tools shapes learning
trajectories over time. In addition, future work should consider how instructors can be best
supported in facilitating evidence-based learning strategies and guiding students to use AI
ethically and effectively within academic contexts.
Ultimately, this dissertation contributes to an evolving conversation about how best to
support learning in an age of rapid technological advancement. It calls for educational practices
that are both cognitively grounded and technologically responsive practices that empower
learners not just to retain information, but to think critically, reflect deeply, and engage actively
with their learning environments. This research lays the groundwork for a more informed,
adaptable, and inclusive vision of education in the digital age by integrating cognitive science,
pedagogical innovation, and emerging AI tools.

130

This dissertation has explored how students learn through cognitive processing,
deliberate practice, and the evolving affordances of AI. At the heart of this inquiry lies a
fundamental tension: how can the science of learning be upheld while responsibly incorporating
emerging educational technologies? Rather than privileging one method over others, this
research illuminates how diverse learning pathways — whether rooted in phonological
awareness, spaced or retrieval practice, or AI-facilitated scaffolding — can support meaningful
learning. Learning is not a linear progression but a dynamic interplay of memory, attention,
strategic regulation, and context. In an era saturated with tools and information, the challenge is
no longer access to knowledge but developing the skills necessary for effective, adaptive
learning.
Across the three empirical studies, this research supports the view that effective learning
is active, cognitively effortful, and anchored in metacognitive awareness. Cognitive flexibility
enables learners to recover from early disruptions; structured techniques such as retrieval
practice and elaboration enhance both long-term retention and metacognition; and AI systems,
when integrated with pedagogical intent, can supplement — but not substitute — critical
thinking. These findings extend beyond theory and offer direct implications for educators,
instructional designers, and learners navigating increasingly complex academic environments.
Looking forward, the imperative is clear: educators must foster adaptable, critically
aware learners who are equipped not only with content knowledge but also with the skills to
evaluate, integrate, and apply what they know. As Huey (1908) anticipated more than a century
ago, the ultimate goal of educational science is not only to understand how learning occurs but to
shape the conditions in which it can thrive. We can better prepare learners for a future that

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demands intellectual rigour and thoughtful adaptability by grounding instruction in cognitive
principles while thoughtfully embracing technological innovation.

132

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Appendix 3. Ethics Approval EO-2024-0508-102.

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Appendix 4. Flashcards
Chapter 9: Intelligence
abstract thinking

capacity to understand hypothetical concepts

bell curve

distribution of scores in which the bulk of the scores falls toward the
middle, with progressively fewer scores toward the “tails” or extremes

between–group
heritability

extent to which the difference in a trait between groups is genetically
influenced

convergent thinking capacity to generate the single best solution to a problem
crystallized
intelligence

accumulated knowledge of the world acquired over time

culture–fair IQ test

abstract reasoning measure that doesn’t depend on language and is often
believed to be less influenced by cultural factors than other IQ tests

deviation IQ

expression of a person’s IQ relative to his or her same–aged peers

divergent thinking

capacity to generate many different solutions to a problem

emotional
intelligence

ability to understand our own emotions and those of others, and to apply
this information to our daily lives

eugenics

movement in the early twentieth century to improve a population’s
genetic stock by encouraging those with good genes to reproduce,
discouraging those with bad genes from reproducing, or both

fluid intelligence

capacity to learn new ways of solving problems

Flynn effect

finding that average IQ scores have been rising at a rate of approximately
three points per decade

g (general
intelligence)

hypothetical factor that accounts for overall differences in intellect among
peo

ideological immune our psychological defences against evidence that contradicts our views
system
intellectual
disability

condition characterized by an onset prior to adulthood, an IQ below about
70, and an inability to engage in adequate daily functioning

intelligence quotient systematic means of quantifying differences among people in their
(IQ)
intelligence
intelligence test

diagnostic tool designed to measure overall thinking ability

mental age

age corresponding to the average individual’s performance on an
intelligence multiple intelligences; idea that people vary in their ability
levels across different domains of intellectual skills

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reliability

consistency of measurement (test–retest reliability refers to the extent to
which scores on a measure remain stable over time)

s (specific abilities)

particular ability level in a narrow domain

Stanford–Binet IQ
test

intelligence test based on the measure developed by Binet and Simon,
adapted by Lewis Terman of Stanford University

stereotype threat

fear that we may confirm a negative group stereotype

test bias

tendency of a test to predict outcomes better in one group than another

triarchic model

model of intelligence proposed by Robert Sternberg positing three
distinct types of intelligence– analytical, practical, and creative

validity

the extent to which a test measures what it purports to measure

Wechsler Adult
Intelligence Scale
(WAIS)

most widely used intelligence test for adults today, consisting of 15
subtests to assess different types of mental abilities

wisdom

application of intelligence toward a common good

within–group
heritability

extent to which the variability of a trait within a group is genetically
influenced

Chapter 10: Human Development
accommodation

Piagetian process of altering a belief to make it more compatible with
experience

adolescence

the transition between childhood and adulthood commonly associated
with the teenage years

assimilation

Piagetian process of absorbing new experience into current knowledge
structures

attachment

the strong emotional connection we share with those to whom we feel
closest

average expectable
environment

environment that provides children with basic needs for affection and
discipline

blastocyst

ball of identical cells early in pregnancy that haven’t yet begun to take on
any specific function in a body part

cognitive
development

study of how children acquire the ability to learn, think, reason,
communicate, and remember

cohort effect

effect observed in a sample of participants that results from individuals in
the sample growing up at the same time

concrete operations
stage

stage in Piaget’s theory characterized by the ability to perform mental
operations on physical events only
155

conservation

Piagetian task requiring children to understand that despite a
transformation in the physical presentation of an amount, the amount
remains the same

Chapter 11: Emotion & Motivation
affective
forecasting

ability to predict our own and others’ happiness

anorexia nervosa

eating disorder associated with excessive weight loss and the irrational
perception that one is overweight

broaden and build
theory

theory proposing that happiness predisposes us to think more openly

bulimia nervosa

eating disorder associated with a pattern of bingeing and purging in an
effort to lose or maintain weight

Cannon–Bard
theory

theory proposing that an emotion–provoking event leads simultaneously to
an emotion and to bodily reactions

cognitive theories
of emotion

theories proposing that emotions are products of thinking

companionate love love marked by a sense of deep friendship and fondness for one’s partner
defensive
pessimism

strategy of anticipating failure and compensating for this expectation by
mentally overpreparing for negative outcomes

discrete emotions
theory

theory that humans experience a small number of distinct emotions that are
rooted in our biology

display rules

cross–cultural guidelines for how and when to express emotions

drive reduction
theory

theory proposing that certain drives, like hunger, thirst, and sexual
frustration, motivate us to act in ways that minimize aversive states

durability bias

belief that both our good and our bad moods will last longer than they do

emotion

mental state or feeling associated with our evaluation of our experiences

excitement phase

phase in human sexual response in which people experience sexual
pleasure and notice physiological changes associated with it

facial feedback
hypothesis

theory that blood vessels in the face feed back temperature information in
the brain, altering our experience of emotions

glucostatic theory

theory that when our blood glucose levels drop, hunger creates a drive to
eat to restore the proper level of glucose

guilty knowledge
test (GKT)

alternative to the polygraph test that relies on the premise that criminals
harbour concealed knowledge about the crime that innocent people don’t

hedonic treadmill

tendency for our moods to adapt to external circumstances
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hierarchy of needs model, developed by Abraham Maslow, proposing that we must satisfy
physiological needs and needs for safety and security before progressing to
more complex needs
homeostasis

equilibrium

incentive theories

theories proposing that we’re often motivated by positive goals

integrity test

questionnaire that presumably assesses workers’ tendency to steal or cheat

internal–external
theory

theory holding that obese people are motivated to eat more by external cues
than internal cues

James–Lange
theory of emotion

theory proposing that emotions result from our interpretations of our bodily
reactions to stimuli

leptin

hormone that signals the hypothalamus and brain stem to reduce appetite
and increase the amount of energy used

motivation

psychological drives that propel us in a specific direction

nonverbal leakage unconscious spillover of emotions into nonverbal behaviour
orgasm (climax)
phase

phase in human sexual response marked by involuntary rhythmic
contractions in the muscles of genitals in both men and women

passionate love

love marked by powerful, even overwhelming, longing for one’s partner

Pinocchio
response

supposedly perfect physiological or behavioural indicator of lying

plateau phase

phase in human sexual response in which sexual tension builds

positive
psychology

discipline that has sought to emphasize human strengths

primary emotions

small number of emotions (perhaps seven) believed by some theorists to be
cross–culturally universal

proxemics

study of personal space

proximity

physical nearness, a predictor of attraction

reciprocity

rule of give and take, a predictor of attraction

resolution phase

phase in human sexual response following orgasm, in which people report
relaxation and a sense of well–being

set point

value that establishes a range of body and muscle mass we tend to maintain

similarity

extent to which we have things in common with others, a predictor of
attraction

somatic marker
theory

theory proposing that we use our “gut reactions” to help us determine how
we should act

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two–factor theory

theory proposing that emotions are produced by an undifferentiated state of
arousal along with an attribution (explanation) of that arousal

Yerkes–Dodson
law

inverted U–shaped relation between arousal on the one hand and mood and
performance on the other hand

Chapter12: Stress, Coping, & Health
acquired immune deficiency a life–threatening, incurable, yet treatable condition in which the
syndrome (AIDS)
human immunodeficiency virus (HIV) attacks and damages the
immune system
acupuncture

ancient Chinese practice of inserting thin needles into more than
2,000 points in the body to alter energy forces believed to run
through the body

aerobic exercise

exercise that promotes the use of oxygen in the body

alternative medicine

health care practices and products used in place of conventional
medicine

biofeedback

feedback by a device that provides almost an immediate output
of a biological function, such as heart rate or skin temperature

biopsychosocial perspective

the view that an illness or medical condition is the product of the
interplay of biological, psychological, and social factors

complementary medicine

health care practices and products used together with
conventional medicine

coronary heart disease
(CHD)

damage to the heart from the complete or partial blockage of the
arteries that provide oxygen to the heart

corticosteroid

stress hormone that activates the body and prepares us to respond
to stressful circumstances

emotion–focused coping

coping strategy that features a positive out– look on feelings or
situations accompanied by behaviors that reduce painful
emotions

fight–or–flight response

physical and psychological reaction that mobilizes people and
animals to either defend themselves (fight) or escape (flee) a
threaten– ing situation

hardiness

set of attitudes marked by a sense of control over events,
commitment to life and work, and courage and motivation to
confront stressful events

health psychology

field of psychology, also called behavioral medicine, that
integrates the behavioral sciences with the practice of medicine

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homeopathic medicine

remedies that feature a small dose of an illness–inducing
substance to activate the body's own natural defenses

immune system

our body's defense system against invading bacteria, viruses, and
other potentially illness-producing organisms and substances

meditation

a variety of practices that train attention and awareness

peptic ulcer

inflamed area in the gastrointestinal tract that can cause pain,
nausea, and loss of appetite

primary appraisal

initial decision regarding whether an event is harmful

proactive coping

anticipation of problems and stressful situations that promotes
effective coping

problem–focused coping

coping strategy by which we problem solve and tackle life's
challenges head–on

psychoneuroimmunology

study of the relationship between the immune system and central
nervous system

psychophysiological

illnesses such as asthma and ulcers in which emotions and stress
contribute to, maintain, or aggravate the physical condition

secondary appraisal

perceptions regarding our ability to cope with an event that
follows primary appraisal

social support

relationships with people and groups that can provide us with
emotional comfort and personal and financial resources

spirituality

search for the sacred, which may or may not extend to belief in
God

stress

the tension, discomfort, or physical symptoms that arise when a
situation, called a stressor— a type of stimulus—strains our
ability to cope effectively

tend and befriend

reaction that mobilizes people to nurture (tend) or seek social
support (befriend) under stress

Chapter 13: Social Psychology
adaptive
conservatism

evolutionary principle that creates a predisposition towards distrusting
anything or anyone unfamiliar or different

aggression

behaviour intended to harm others, either verbally or physically

altruism

helping others for unselfish reasons

attitude

belief that includes an emotional component

attribution

process of assigning causes to behaviour
159

belief

conclusion regarding factual evidence

cognitive
dissonance

unpleasant mental experience of tension resulting from two conflicting
thoughts or beliefs

conformity

tendency of people to alter their behaviour as a result of group pressure

cults

groups of individuals who exhibit intense and unquestioning devotion to a
single cause

deindividuation

tendency of people to engage in uncharacteristic behaviour when they are
stripped of their usual identities

diffusion of
responsibility

reduction in feelings of personal responsibility in the presence of others

diffusion of
responsibility

reduction in feelings of personal responsibility in the presence of others

discrimination

negative behaviour towards members of out–groups

door–in–the–face
technique

persuasive technique involving making an unreasonably large request
before making the small request we are hoping to have granted

enlightenment
effect

learning about psychological research can change real–world behaviour for
the better

explicit prejudice

unfounded negative belief of which we are aware regarding the
characteristics of an out–group

foot–in–the–door
technique

persuasive technique involving making a small request before making a
bigger one

fundamental
attribution error

tendency to overestimate the impact of dispositional influences on other
people's behaviour

group polarisation tendency of group discussion to strengthen the dominant positions held by
individual group members
groupthink

emphasis on group unanimity at the expense of critical thinking and sound
decision–making

implicit prejudice

unfounded negative belief of which we are unaware regarding the
characteristics of an out–group

impression
management
theory

theory that we do not really change our attitudes, but report that we have so
that our behaviours appear consistent with our attitudes

inoculation effect

approach to convincing people to change their minds about something by
first introducing reasons why the perspective might be correct and then
debunking it

in–group bias

tendency to favour individuals within our group over those from outside
our group
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jigsaw classroom

educational approach designed to minimise prejudice by requiring all
children to make independent contributions to a shared project

just–world
hypothesis

claim that our attributions and behaviours are shaped by a deep–seated
assumption that the world is fair and all things happen for a reason

low–ball technique persuasive technique in which the seller of a product starts by quoting a
low sale price, and then mentions all of the add–on costs once the customer
has agreed to purchase the product
mass hysteria

outbreak of irrational behaviour that is spread by social contagion

obedience

adherence to instructions from those of higher authority

out–group
homogeneity

tendency to view all individuals outside our group as highly similar

paramedic studies

studies in which an experimenter systematically manipulates the
independent variable to observe its effects on the dependent variable

pluralistic
ignorance

error of assuming that no one in a group perceives things as we do

prejudice

drawing conclusions about a person, a group of people or a situation prior
to evaluating the evidence

relational
aggression

form of indirect aggression, prevalent in girls, involving spreading
rumours, gossiping and nonverbal putdowns for the purpose of social
manipulation

scapegoat
hypothesis

claim that prejudice arises from a need to blame other groups for our
misfortunes

self–monitoring

personality trait that assess the extent to which people's behaviour reflects
their true feelings and attitudes

self–perception
theory

theory that we acquire our attitudes by observing our behaviours

social comparison
theory

theory that we seek to evaluate our beliefs, attitudes and abilities by
comparing our reactions with those of others

social facilitation

enhancement of performance brought about by the presence of others

social loafing

phenomenon whereby individuals become less productive in groups

social psychology

study of how people influence others' behaviour, beliefs and attitudes

stereotype

a belief, positive or negative, about the characteristics of members of a
group that is applied generally to most members of the group

ultimate attribution assumptions that behaviours among individual members of a group are due
error
to their initial dispositions

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Chapter 14: Personality
anal stage

psychosexual stage that focuses on toilet training

archetype

cross–culturally universal symbols

Big Five

five traits that have surfaced repeatedly in factor analyses of
personality

collective unconscious

according to Jung, our shared storehouse of memories that ancestors
have passed down to us across generations

conditions of worth

according to Rogers, expectations we place on ourselves for
appropriate and inappropriate behavior

defense mechanisms

unconscious maneuvers intended to minimize anxiety

denial

motivated forgetting of distressing external experiences

displacement

directing an impulse from a socially unacceptable target onto a safer
and more socially acceptable target

ego

psyche's executive and principal decision maker

Electra complex

conflict during phallic stage in which girls supposedly love their
fathers romantically and want to eliminate their mothers as rivals

empirical method of test
construction

approach to building tests in which researchers begin with two or
more criterion groups, and examine which items best distinguish
them

erogenous zone

sexually arousing zone of the body

face validity

extent to which respondents can tell what the items are measuring

factor analysis

statistical technique that analyzes the correlations among responses
on personality inventories and other measures

genital stage

psychosexual stage in which sexual impulses awaken and typically
begin to mature into romantic attraction toward others

graphology

psychological interpretation of handwriting

id

reservoir of our most primitive impulses, including sex and
aggression– completely unconscious

identification with the
aggressor

process of adopting the characteristics of individuals we find
threatening

idiographic approach

approach to personality that focuses on identifying the unique
configuration of characteristics and life history experiences within a
person

incongruence

inconsistency between our personalities and innate dispositions

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incremental validity

extent to which a test contributes into beyond other, more easily
collected, measures

inferiority complex

feelings of low self–esteem that can lead to overcompensation for
such feelings

latency stage

psychosexual stage in which sexual impulses are submerged into the
unconscious

lexical approach

approach proposing that the most crucial features of personality are
embedded in our language

locus of control

extent to which people believe that reinforcers and punishers lie
inside or outside of their control (internals– life events are due to
own efforts, externals– life events are product of fate)

MMPI

The Minnesota Multiphasic Personality Inventory – widely used
structured personality test designed to assess symptoms of mental
disorders

molecular genetic study

investigation that allows researchers to pinpoint genes associated
with specific personality traits

Neo–Freudian theories

theories derived from Freud's model, but that placed less emphasis
on sexuality as a driving force in personality and were more
optimistic regarding the prospects for long–term personality growth

nomothetic approach

approach to personality that focuses on identifying general laws that
govern the behavior of all individuals

Oedipus complex

conflict during phallic stage in which boys supposedly love their
mothers romantically and want to eliminate their fathers as rivals

oral stage

psychosexual stage that focuses on the mouth

P.T. Barnum effect

tendency of people to accept high base rate descriptions as accurate

peak experience

transcendent moment of intense excitement and tranquility marked
by a profound sense of connection to the world

personality

people's typical ways of thinking, feeling, and behaving

phallic stage

psychosexual stage that focuses on the genitals

pleasure principle

tendency of the id to strive for immediate gratification

projection

unconscious attribution of our negative characteristics to others

projective hypothesis

hypothesis that in the process of interpreting ambiguous stimuli,
examinees project aspects of their personality onto the stimulus

projective test

test consisting of ambiguous stimuli that examinees must interpret or
make sense of

psychic determinism

the assumption that all psychological events have a cause
163

rational/theoretical
method of test
construction

approach to building tests that requires test developers to begin with
a clear–cut conceptualization of a trait and then write items to assess
that conceptualization

rationalization

providing a reasonable–sounding explanation for unreasonable
behaviors or for failures

reaction–formation

transformation of an anxiety–provoking emotion into its opposite

reality principle

tendency of the ego to postpone gratification until it can find an
appropriate outlet

reciprocal determinism

tendency for people to mutually influence each other's behaviors

regression

the act of returning psychologically to a younger, and typically
simpler and safer, age

repression

motivated forgetting of emotionally threatening memories or
impulses

Rorschach Inkblot test

projective test consisting of ten symmetrical inkblocks

self–actualization

drive to develop our innate potential to the fullest possible extent

social learning theorists

theorists who emphasize thinking as a cause of personality

structured personality
test

paper/pencil test consisting of questions that respondents answer in
one of a few fixed ways

style of life

according to Adler, each person's distinctive way of achieving
superiority

sublimation

transforming a socially unacceptable impulse into an admired goal

superego

our sense of morality

TAT

Thematic Apperception Test – projective test requiring examinees to
tell a story in response to ambiguous pictures

trait

relatively enduring predisposition that influences our behavior across
many situations

Chapter 15: Psychological Disorders
ADHD

childhood condition marked by excessive inattention, impulsivity, and
activity

agoraphobia

fear of being in a place or situation from which escape is difficult or
embarrassing, or in which help is unavailable in the event of a panic
attack

antisocial personality condition marked by a lengthy history of irresponsible and/or illegal
disorder
actions
164

anxiety sensitivity

fear of anxiety–related sensations

asylum

institution for people with mental illnesses created in the 15th century

autistic disorder

disorder marked by severe deficits in language, social bonding, and
imagination, usually accompanied by mental retardation

axis

dimension of functioning

bipolar disorder

condition marked by a history of at least one manic episode

borderline
personality disorder

condition marked by extreme instability in mood, identity, and impulse
control

catatonic symptom

motor problem, including extreme resistance to complying with simple
suggestions, holding the body in bizarre or rigid postures, or curling up
in a fetal position

categorical model

model in which a mental disorder differs from normal functioning in
kind rather than degree

cognitive model of
depression

theory that depression is caused by negative beliefs and expectations

comorbidity

co–occurrence of two or more diagnoses within the same person

compulsion

repetitive behavior or mental act performed to reduce or prevent stress

deinstitutionalization governmental policy that focused on releasing hospitalized psychiatric
patients into the community and closing mental hospitals
delusion

strong held, fixed belief that has no basis in reality

demonic model

view of mental illness in which odd behavior, hearing voices, or talking
to oneself was attributed to evil spirits infesting the body

depersonalization
disorder

condition marked by multiple episodes od depersonalization

diathesis–stress
model

perspective proposing that mental disorders are a joint product of a
genetic vulnerability, called a diathesis, and stressors that trigger this
vulnerability

dimensional model

model in which a mental disorder differs from normal functioning in
degree rather than kind

dissociative amnesia cant recall important personal info
dissociative disorder condition involving disruptions in consciousness, memory, identity, or
perception
dissociative fugue

drop everything and flee stressful circumstance

dissociative identity
disorder

multiple personalities

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DSM

diagnostic system containing the American Psychiatric Association
criteria for mental disorders

generalized anxiety
disorder

continual feelings of worry, anxiety, physical tension, and irritability
across many areas of life functioning

hallucination

sensory perception that occurs in the absence of an external stimulus

hypochondriasis

an individuals continual preoccupation with the notion that he has a
serious physical disease

insanity defense

legal defense proposing that people shouldn't be help legally responsible
for their actions if they weren't of sound mind" when committing them"

involuntary
commitment

procedure of placing some people with mental illnesses in a psychiatric
hospital or other facility based on their potential danger to themselves or
others, or their inability to care for themselves

labeling theorists

scholars who argues that psychiatric diagnoses exert powerful negative
effects on people's perceptions and behaviors

learned helplessness

tendency to feel helpless in the face of events we can't control

major depressive
episode

state in which a person experiences a lingering depressed mood or
diminished interest in pleasurable activities, along with symptoms that
include weight loss and sleep difficulties

manic episode

experience marked by dramatically elevated mood, decreased need for
sleep, increased energy, inflated self–esteem, increased talkativeness,
and irresponsible behavior

medical model

view of mental illness as due to a physical disorder requiring medical
treatment

moral treatment

approach to mental illness calling for dignity, kindness, and respect for
those with mental illness

obsession

persistent idea, thought, or impulse that is unwanted and inappropriate,
causing marked distress

OCD

condition marked by repeated and lengthy immersion in obsessions,
compulsions, or both

panic attack

brief, intense episode of extreme fear characterized by sweating,
dizziness, light–headedness, racing heartbeat, and feelings of impending
death or going crazy

panic disorder

repeated and unexpected panic attacks, along with either persistent
concerns about future attacks or a change in personal behavior in an
attempt to avoid them

personality disorder

condition in which personality traits, appearing in adolescence, are
inflexible, stable, expressed in a wide variety of situations, and lead to
distress or impairment
166

phobia

intense fear of an object or situation that's greatly out of proportion to its
actual threat

prevalence

percentage of people within a population who have a specific mental
disorder

psychopathic
personality

condition marked by superficial charm, dishonesty, manipulativeness,
self–centeredness, and risk taking

psychotic symptom

psychological problem reflecting serious disortions in reality

PTSD

marked emotional disturbance after experiencing or witnessing a
severely stressful event

schizophrenia

severe disorder of thought and emotion associated with a loss of contact
with reality

social phobia

marked fear of public appearances in which embarrassment or
humiliation seems likely

somatoform disorder condition marked by physical symptoms that suggest an underlying
medical illness, but that are actually psychological in orgin
specific phobia

intense fear of objects, places, or situations that is greatly out of
proportion to their actual threat

Chapter 16: Psychological and Biological Treatments
Alcoholics anonymous

Twelve–step self–help program that provides social support for
achieving sobriety

Aversion therapy

Treatment that uses punishment to decrease the frequency of
undesirable behaviors

Behavior therapist

Therapist who focuses on specific problem behaviors and current
variables that maintain problematic thoughts, feelings, and behavior

Cognitive–Behavioral
therapy

Treatment that attempts to replace irrational cognitions and
maladaptive behaviors with more rational cognitions and adaptive
behaviors

Dismantling

Research procedure for examining the effectiveness of isolated
components of a larger treatment

Ecological momentary
assessment

Assessment of thoughts, emotions, and behaviors that arise in the
moment in situations in which they occur in everyday life

Electroconvulsive
therapy (ECT)

Treatment for serious psychological problems in which patients
receive brief electrical pulses to the brain that produce a seizure

Empirically supported
treatment (EST)

Intervention for specific disorders supported by high–quality
scientific evidence
167

Exposure therapy

Therapy that confronts clients with what they fear with the goal of
reducing the fear

Free association

Technique in which clients express themselves without censorship
of any sort

Gestalt therapy

Therapy that aims to integrate different and sometimes opposing
aspects of personality into a unified sense of self

Group therapy

Therapy that treats more than one person at a time

Humanistic therapies

Therapies that emphasize the development of human potential and
the belief that human nature is basically positive

Insight therapies

Psychotherapies, including psychodynamic, humanistic, existential,
and group approaches, with the goal of expanding awareness or
insight

Interpersonal therapy
(IPT)

Treatment that strengthens social skills and targets interpersonal
problems, conflicts, and life transition

Meta–analysis

Statistical method that helps researchers interpret large bodies of
psychological literature

Paraprofessional

Person with no professional training who provides mental health
services

Participant modeling

Technique in which the therapist first models a problematic
situation and then guides the client through steps to cope with it
unassisted

Personalized medicine

Medical practice that customizes interventions to maximize success
in treating patients with specific psychological or medical disorders
and conditions

Person–centered therapy

Nondirective therapy centering on the client's goals and ways of
solving problems

Psychopharmacotherapy

Use of medications to treat psychological problems

Psychosurgery

Brain surgery to treat psychological problems

Psychotherapy

Psychological intervention designed to help people resolve
emotional, behavioral, and interpersonal problems and improve the
quality of their lives

Resistance

Attempts to avoid confrontation and anxiety associated with
uncovering previously repressed thoughts, emotions, and impulses

Response prevention

Technique in which therapist prevent clients from performing their
typical avoidance behaviors

Strategic family
intervention

Family therapy approach designed to remove barriers to effective
communication
168

Structural family therapy Treatment in which therapists deeply involve themselves in family
activities to change how family members arrange and organize
interactions
Systematic
desensitization

Clients are taught to relax as they are gradually exposed to what
they fear in a stepwise manner

Token economy

Method in which desirable behaviors are rewarded with tokens that
clients can exchange for tangible rewards

Transference

Act of projecting intense, unrealistic feelings and expectations from
the past onto the therapist

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Appendix 5. Ethics Approval 6009220 (2)

170