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Learning tactic use among middle school students with and without ADHD in an online environment

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Learning tactic use among middle school students with and without ADHD in an online environment

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Content Running Head: NSTUDY FOR ADHD i

LEARNING TACTIC USE AMONG MIDDLE SCHOOL STUDENTS
WITH AND WITHOUT ADHD IN AN ONLINE ENVIRONMENT

by

Nicole Karolina Saloun

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

May 2015

Running Head: NSTUDY FOR ADHD ii

Acknowledgments
To Dr. Gale Sinatra, who said “Yes.” Over SKYPE on New Year’s Eve she agreed to
lead me on this crazy and sometimes improbable journey for which I will forever be grateful.
To her, this project was never a hoop through which to be jumped, but a meaningful
exploration into new territory. Words cannot express how much I appreciate her willingness to
persevere through an alphabet of plans for this study.
To Drs. Yates and Keim, who have enlightened and nurtured both as professors and as
committee members. I have been lucky to benefit from their expertise and vision in both
capacities.
To Bobby Danielson, who surely could not have known what he was getting into. His
intellect and his talent for explanation are not short of startling.
To Dr. Rudolph Crew, who taught me how to interact with people. Apart from courting
Dr. Sinatra, one of the best decisions I made in this program was to request placement in his
course.
To Dr. Phil Winne and Liam Doherty for their generous contributions of software and
the purest gift of all: their time.
To Caleb who slept on a couch out of solidarity. He ate chili from a can, cleaned
refrigerators, sacrificed vacations, stapled correlation matrices, and dried tears so I could be a
doctor. To my love and my life, I will be honored to become Dr. Claxton.
To my parents who have uttered not a word of dismay, even when told that the joy of
May, 2013 would be deferred to May, 2014. They supported the goals, the efforts, the
Running Head: NSTUDY FOR ADHD iii

accomplishments and the purposes of my participation in this program even when they were
not so clear to me.
To Dr. Kristina Pappas for her constant evisceration of my study design, the current
version of which owes not only to her brilliance, but also to her delicate balance of skepticism
and support.
To Tom Keenoy with Medium Rare Interactive for continuing to be consistently
awesome at his job and for always fitting tweaks into his calendar.
To Viv Penninti, who genuinely seems to enjoy explaining statistics, even to one of the
most difficult of statistics students.
To Scott Smith, Rebecca Haggarty, and Heathers Davis and Brunold for the
camaraderie and collaboration during the first years of the program. Both were of the highest
caliber. Special thanks to Scott for leading Friday Night Writes, for sharing his awe-inspiring
visual skills, and for his all-around creativity and unwavering technical support. If not for him,
I would have had to type citations.
Thank you.

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Table of Contents

Acknowledgments ii
List of Tables vii
List of Figures viii
Abstract ix

Chapter One: Introduction 1
Self-Regulated Learning 2
ADHD and Executive Functioning 3
nStudy 5
Statement of the Problem 6
Purpose and Overview of the Study 7
Research Questions and Hypotheses 9
Methods 11
Results 12
Organization 12

Chapter Two: Literature Review 13
Self-Regulated Learning 14
Theoretical Foundations of Self-Regulated Learning 14
Self-Monitoring and Self-Monitoring Prompts 15
Prior Knowledge as Part of SRL 16
Learning Strategy Use 17
The Four-Phase Model 20
Issues of Measurement of SRL 21
Incomplete Operationalization of SRL and Issues of Internal Validity 21
The Nature of Self-Reports and Issues of Internal Validity 24
Threats to External Validity 26
ADHD and Executive Functioning 27
Diagnosis and Prevalence 27
Executive Functioning 29
A Note about ADHD, Hyperactive-Impulsive Type 30
Self-Regulated Learning among Students with ADHD 31
Academic Enjoyment 33
Motivation among Students with ADHD 34
nStudy 35
Summary 36

Chapter Three: Methods 38
Participants 38
Materials 40
Qualitative Reading Inventory-5 40
Reading fluency 40
Prior knowledge 41
Passages 42
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Reading Comprehension Questions 42
The Vanderbilt ADHD Diagnostic Parent Rating Scale 43
Procedures 43
Prior Knowledge 44
Reading Fluency 45
nStudy Training and Instructions 45
Self-Monitoring and Enjoyment Prompts 46
Quiz and Questions 46
Parent Survey 47
Data Analysis 47
Tactic Use 47
Scoring the QRI-5 48
Word lists 48
Oral passage reading 48
Prior knowledge 48
Reading comprehension 49
Quantitative Analysis 49

Chapter Four: Results 50
Demographic Variables 51
Ratings of Symptoms of ADHD on VADPRS 52
Reading Ability 52
Prior Knowledge 53
Study Tactic Use among Students in Both Groups 53
Self-Monitoring Prompts 58
Learning Outcome 59
Tactic Use 60
Variables Other than Tactic Use 60
Enjoyment 61
Students without ADHD 62
Students with ADHD 62

Chapter Five: Discussion 63
Interpretation of Findings for Research Questions 63
Tactic Use in the Two Groups 63
ADHD and risk-taking 64
The glossary hypothesis 66
Self-Monitoring Prompts 67
Learning Outcome 68
Enjoyment 71
Measurement of Self-Regulated Learning as an Event 72
Considerations and Limitations 73
Sample Population 73
Two Students with ADHD, Hyperactive Type 74
Passage Difficulty 75
Hawthorne Effect 75
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Implications 75
Applications of Winne and Hadwin’s Four-Phase Model 77
Directions for Future Research 78
nStudy as a Data Collection Tool to Profile a Student’s SRL 78
Prior Knowledge 78
Enjoyment 79
A More Difficult Passage 79
Neurological Correlates of ADHD 80
Summary 80

References 93

Appendices 108
Appendix A: USC IRB Approval Letter 108
Appendix B: Student Word Lists 110
Appendix C: Examiner Word Lists 111
Appendix D: Oral Reading Fluency Passage 112
Appendix E: Prior Knowledge Assessment 113
Appendix F: Stars Passage 114
Appendix G: Reading Comprehension Quiz 116
Appendix H: Vanderbilt ADHD Diagnostic Parent Rating Scale 118
Appendix I: nStudy Training Pages 121
Appendix J: nStudy Quick Reference Guide 126
Appendix K: Self-monitoring and Enjoyment Prompt Pages 127
Appendix L: Parent Survey 129

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List of Tables
Table 1: Summary of reported demographic information 82
Table 2: Parametric correlation matrix for students without ADHD 83

Table 3: Parametric correlation matrix for students with ADHD 84

Table 4: Non-parametric correlation matrix for students without ADHD 85

Table 5: Non-parametric correlation matrix for students with ADHD 86

Table 6: Tactic use: Frequency and number of students 87

Table 7: Summary of responses to follow-up questions about tactic use 88

Table 8: Tactic use and enjoyment before and after self-monitoring prompt 89

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List of Figures

Figure 1: Tactic use and number of students in non-ADHD and ADHD groups 90

Figure 2: Response to self-monitoring prompt 91

Figure 3: Prior knowledge and quiz scores for students without ADHD 92

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Abstract
This study uses the operationalization of self-regulated learning “as an event” to analyze the
frequency and type of learning strategy use among students with and without ADHD. An
observational design was used to capture a snapshot of seventh- and eighth-grade students’
behavior in an authentic study-for-a-test learning context; data analysis compared students with
ADHD to more typical students. Though the small sample size was small, findings were
consistent with expectations, as students with ADHD used fewer learning strategies (referred to
as tactics) and relied were reluctant to employ more generative tactics compared to students
without ADHD. Results were not significant in either group for students’ response to a self-
monitoring prompt. Results were also nonsignificant for the relationship between tactic use
and learning outcome for either group; however students without ADHD demonstrated a
statistically significant relationship between prior knowledge and quiz score, whereas students
with ADHD did not. There was a significant negative relationship between enjoyment and
tactic use for students with ADHD but not for students without ADHD. This study contributes
to literature on the learning behaviors of students with ADHD and introduced a more direct
measure to the literature on the measurement of self-regulated learning than the self-reports
typically used.

.

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CHAPTER ONE: INTRODUCTION
One of the great mysteries for parents of adolescents and pre-adolescents is what goes
on behind closed doors when their children shout, "I'm studying!" In fact, studying remains a
mystery for many parents and students well into the high school years and often into college.
A key difference that separates the successful from the struggling is study behavior. The term
"studying" itself holds a tremendous range of meaning for students. For a student in medical
school, studying involves self-testing on key terms and definitions, as well as peer testing on
clinical scenarios. For a young soccer player, studying can mean hours of practice followed by
watching and analyzing videos of himself and of professionals. For a middle school student,
studying often means staring at a page hoping to memorize information without much thought
about how to organize information or measure the progress of learning.
Regular curricular testing represents a traditional and prevalent form of assessment in
American schools. Depending on the school, most children begin to incorporate the task of
studying into their weekly routines by late elementary school, when they take on at-home
responsibility for learning basic, declarative information like new vocabulary words, the 50
states and even information about tectonic plate boundaries and other scientific facts. How a
student takes on the task of studying depends largely on the structure imposed by both his
school and his parents, but individual traits play an important role as well. In addition to
affective characteristics, cognitive functioning and motivational sources also determine the
strategies a student employs, the amount of time he dedicates to study tasks, and his
willingness to sit down to the task at all.

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Self-Regulated Learning
Self-regulated learning (SRL) can be operationalized as an awareness of thinking and
understanding (metacognition), plus subsequent strategic engagement in the learning process,
including monitoring of progress toward learning goals (Butler, 2002; Pintrich & De Groot,
1990; Torrano & Torres, 2004; Zimmerman & Martinez-Pons, 1988). Flavell (1979)
developed the first model of metacognitive knowledge, awareness of one's knowledge and
learning, which has since been incorporated into contemporary views of learning taxonomy as
a dimension of learning beyond declarative, conditional and procedural knowledge (Anderson,
Krathwohl, & Bloom, 2005). Zimmerman (2001) synthesized the many influences on self-
regulated learning, including information processing models, constructivist views, behavior
contributions and motivational components. His work is largely grounded in Bandura's social
cognitive theory of learning (Bandura, 1986), including his description of the reciprocal
relationships among three components: learner, behavior and environment. Within this
foundation, Zimmerman pioneered early efforts to measure self-regulated learning using
teacher ratings, achievement test scores and student interviews (Zimmerman & Martinez-Pons,
1986; 1988). Pressley applied body of SRL research to reading found that self-regulated
readers tend to have strong metacognitive awareness, a sense of purpose for which they are
reading, and they have "a set of tentative plans or strategies for handling potential problems
and for monitoring their comprehension of textual information," including summarizing and
clarifying (Pressley, 1995, as cited in Mokhtari & Reichard, 2002, p. 249). Congruent with the
weaknesses in executive functioning among students with ADHD, that group also struggles
with metacognitive awareness necessary for SRL, particularly while reading (Alvarado,
Puente, Jiménez, & Arrebillaga, 2011).
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ADHD and Executive Functioning
For a student with Attention Deficit/Hyperactivity Disorder (ADHD), school-related
challenges - especially those that require independent self-directed study - are magnified.
ADHD is a disorder of the prefrontal cortex, which, depending on subtype, results in a pattern
of impulsivity and delays and deficits in the development of executive functions. Estimates of
the prevalence of ADHD among school-aged children in the U.S. range from about 4% to 9%
(R. Guare, Dawson, & Guare, 2013; Visser, 2013). Though twin studies have revealed a
genetic component to ADHD, specific genes have not yet been identified (Willcutt,
Pennington, & DeFries, 2000).
Broadly speaking, executive functions are the highest-order cognitive functions; the
ones that set humans apart from other species that share our DNA in our ability to plan,
evaluate, strategize, monitor and control our behavior. Early models (Pennington, 1997, as
cited in Nigg, Blaskey, Huang-Pollock, & Rappley, 2002) described executive functions as a
regulation of response within a context and the maintenance of behavior toward achieving a
goal. They identified motor inhibition, planning, interference control, and set-shifting as
specific skills in that domain. One of the most salient elements of ADHD is difficulty with
inhibitory control, which presents as trouble inhibiting responses. Casey et al. (1997)
subjected participants to three tasks to represent three aspects of response inhibition: sensory
selection, response selection and response execution. The found not only that subjects with
ADHD performed more poorly on the tasks, but also that for those participants, the
frontostriatal brain structures, which are associated with response inhibition, have significantly
less volume than for those of their same-aged peers.
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In academic settings, these difficulties manifest themselves in trouble focusing in
school, completing homework, organizing materials and managing time (American Psychiatric
Association, 2013). For students with ADHD, these difficulties are often associated with their
performance in school and have even been found to correlate with their grade point average
(Kent et al., 2010; Langberg et al., 2011). Several researchers (Barkley, 1997; 2013; Meltzer,
2011) have focused on the role of executive functioning in ADHD; Barkley (1997) has
identified four executive functions that appear to depend on behavioral inhibition for their
effective execution: (a) working memory, (b) self-regulation of affect-motivation-arousal, (c)
internalization of speech, and (d) reconstitution (behavioral analysis and synthesis). Working
memory is a key individual student characteristic that plays a significant role in encoding
information into long-term memory stores for later retrieval. In other words, it is a phase of
memory in which a learner must hold onto information as he decides how best to organize it;
for people with ADHD who suffer from weak working memory, information is particularly
vulnerable to poor encoding (misfiling) or even complete loss during this phase. Self-
regulation of affect-motivation-arousal refers to the ability to manage the emotional aspects of
learning, which plays an important role in the motivational aspect of SRL. For example, a
student reads a paragraph, does not understand it, and feels guilt or annoyance or shame about
not understanding it. Instead of re-reading that paragraph, going back further in the text to
clarify a concept, or looking up an unfamiliar word, a student with ADHD is more likely to
skip that paragraph and either continue reading and "hope for the best" or cease the task
because it became to difficult or boring. Internalization of speech here refers to one's internal
dialogue that engages in a feedback loop about the effectiveness of learning. Reconstitution
refers to the actions in which a learner engages in response to the information generated during
Running Head: NSTUDY FOR ADHD 5

the feedback loop. These four executive functions facilitate sustained attention, which
essentially refers to the ability to apply enough mental effort and concentration to focus on a
task (Barkley, 1997).
nStudy
Neuropsychological measures of executive functioning, such as the tower building task
on the D-KEFS or the Wisconsin Card Sort, which require students to respond flexibly and
strategically to changing conditions, offer data about present levels of cognitive abilities in a
single task in a context that is often dissimilar to curricular demands. For example, a student
who can complete a tower-building task may have the ability to set a task-based goal in a
clinical setting, but may flounder when faced with the goal of preparing for a unit test on cell
replication.
nStudy is a web-based internet application that supports learners in engagement with
online content; specifically it provides functionality to allow students to enact learning tactics,
such as: notes, terms, tags, concept maps, chats and rich text documents. nStudy is able to
capture a snapshot of a student's typical implementation of executive functions in educational
contexts; to date, there are no other known commercially available instruments that allow a
practitioner to measure directly or describe a student's study behavior in preparation for a test.
In clinical settings a psychologist, educational therapist or special education teacher typically
wants to collect qualitative data on the study habits of a student with ADHD, or a student or
who has demonstrated weaknesses in executive functioning.
This study uses the nStudy online environment to mimic the task of studying in
preparation for a typical curricular assessment on a relatively difficult passage. Beyond its
portability, the unique benefits of nStudy as a tool to facilitate the collection of data about SRL
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include its lack of intrusiveness (in contrast to structured questionnaires and think-aloud
protocols require that students be interrupted in order to narrate their processes), its
customizability (in that it functions as a shell into which a user can load any electronic text),
and its ability to gather information about automatized behavior (as opposed to only
information about behaviors of which the learner is aware).
Statement of the Problem
By their nature, metacognition, self-regulated learning and the entire family of
executive functions remain some of the most difficult educational constructs to measure
(Winne, Jamieson-Noel, & Muis, 2002). Even instruments that have been validated as
measures of executive functions are often found to measure other constructs as well
inadvertently. Like reading, cognitive processes like attention, self-regulation, and memory are
complex relationships among overlapping constructs that function together dynamically. On a
practical level, the task of studying in preparation for a test, which represents the epitome of
self-regulated learning, is perhaps the most important and universal skill associated with being
a student. Yet for decades, students, parents and teachers have lamented the elusive quality of
this individual and at times frustratingly private task.
At their best, current measures of executive functioning and self-regulated learning
capture the planning, strategizing, organizing and other similar skills that the student can
perform, but often lack the ecological validity to capture what students do perform. The
SRLIS was the first of its kind used to measure SRL, and most subsequent measures have also
been checklists, interviews, and other measures that ask a learner to describe his or her own
SRL. Though they may generate a reliable gestalt picture of a student’s approach to learning,
and therefore predict academic achievement, self-reports of SRL seem to omit critical data
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about in-the-moment employment of learning tactics. nStudy allows researchers to capture data
from traces of students' SRL, which no extant instrument or tool does.
Purpose and Overview of the Study
This study addresses several gaps in literature and a prominent gap in clinical
evaluation of SRL. Overwhelmingly, literature on students’ SRL relies on self-reports,
whereas this study seeks to assess SRL directly and authentically in a context that simulates
studying for a test. Relatively little research has been done on the use and effectiveness of
learning strategies among adolescents with ADHD, and because of their poorer executive
functioning (which encompasses self-monitoring and self-awareness), they are likely to be
particularly weak self-reporters (Smith, Pelham, Gnagy, Molina, & Evans, 2000).
In response to the increasing use of e-texts and online learning environments, this study
required students to employ tactics available through software that functions in tandem with
hypertext. Literature on the effectiveness of self-reports has been mixed, but again little
research has been published about the responses of students with ADHD to such prompts to
employ learning strategies. Finally, because of the nebulous nature of “enjoyment” among
studies or the tendency to focus on general reported academic enjoyment, this study sought to
capture a real-time assessment of enjoyment among students with and without ADHD in an
academic context.
There are three potential contributions of this study: (a) information about the presence
or absence of differences among students with and without ADHD on measures of SRL in
nStudy can facilitate the development of nStudy as a tool to measure and train SRL; (b) if the
hypotheses about the differences in SRL behavior between students with and without ADHD
are supported, a diagnostician (e.g. a psychologist, educational therapist or special education
Running Head: NSTUDY FOR ADHD 8

teacher) can use nStudy to gather qualitative data about a student's SRL, which can then be
used to make referrals for neuropsychological evaluation, set academic and cognitive goals,
and evaluate progress toward them; (c) this study can contribute to the literature about the use
of learning strategies, particularly the choice of learning tactics, as well as the reported
enjoyment among students with ADHD, The SRL patterns among students with ADHD can
provide evidence about that population's application of strategies immediately following
training in use of specific tactics (which is a step in the study procedure before SRL data are
gathered). In other words, if students with ADHD do not use SRL strategies much, even in a
measurement context immediately following training in tactics, educators and parents can
better understand the degree of the impact of ADHD on academic performance. If they do
engage meaningfully in SRL tactics, educators and parents can provide training in SRL tactics
immediately before study tasks to improve the quality of studying. A recent study used the
Learning and Study Strategies Inventory (LASSI) to compare the SRL of college students with
ADHD, LD and traditional learners and drew two important conclusions relevant to this study.
First, they concluded that the group of students with ADHD indicated weaker SRL in four
areas: time management, concentration, selecting main ideas, and test strategies. Second, they
concluded that the LASSI was not an appropriate tool to measure SRL among students with
ADHD because it accounted for 22% of the variance in college grades for the traditional
students and did not predict college grades for the students with ADHD. Because the LASSI is
a self-report measure of habitual behavior, it represents the measures most commonly used to
measure SRL and that this study claims do not accurately represent a student's actual use of
strategies in a learning context that mimics typical student preparation for scholastic tests.
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Furthermore, relationships between enjoyment and tactic use can inform instruction
both in content areas and in learning strategy use itself. If these relationships are different for
the two groups, differentiated instruction could be indicated.
Research Questions
The study addressed the following research questions:
1. Does study tactic use differ between students with and without ADHD when
studying for a test in an online learning environment?
1a.What specific behaviors characterize the process of studying for students with
and without ADHD?
2. Do self-monitoring prompts during studying affect the use of study tactics
differently between the two student groups?
3. Is tactic use associated with learning outcomes, as measured by the criterion-
referenced quiz?
4. Is self-reported enjoyment before and after the prompt associated with tactic use for
one or both of the groups?
Research Hypotheses
1. Though quantity of tactic use does not equate to sophistication or quality of SRL,
research has found metacognitive variables to be correlated with use of tools in an
online learning environment (Juarez Collazo, Corradi, Elen, & Clarebout, 2014).
Furthermore, because students with ADHD consistently demonstrate such profound
weaknesses in SRL, it is expected that overall, this group will likely use fewer total
tactics than those in the non-ADHD group.
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1a. Students without ADHD, who are therefore likely to have stronger executive
functions, may engage more elaborative tactics that involve self-explanation like
creating notes, selecting tags, and visiting other websites. Students with ADHD
may be more likely to engage in more passive strategies like highlighting and
glossary use.
2. Though literature on self-monitoring prompts is mixed, given the clinical setting
and the fact that students without ADHD are more adept at incorporating and
responding to feedback (Shiels & Hawk, 2010), both groups should demonstrate an
increase in tactic use in the second half of the passage, though students without
ADHD are probably more likely to respond with a greater increase.
3. Though some students without ADHD may employ more tactics than they need out
of hyper vigilance, tactic use – particularly elaborative tactic use – should predict
learning outcomes for both groups, particularly since reading level and prior
knowledge are being accounted for. Research findings from one study among
graduate students have been consistent with this hypothesis (Juarez Collazo et al.,
2014).
4. The literature on enjoyment, and related concepts like interest, engagement and
flow, does not evince a clear association with learning behaviors, it is difficult to
predict how students will respond affectively to the nStudy tasks. Because research
on traditional students typically reveals correlations between engagement and
learning, students without ADHD may display a positive correlation both before
and after the self-monitoring prompt. However, since students with ADHD tend to
eschew academic challenge and tend to report a lack of enjoyment of reading, they
Running Head: NSTUDY FOR ADHD 11

may display a negative correlation between enjoyment and tactic use both before
and after. Due to difficulty sustaining attention and resultant test fatigue, students
with ADHD may show a stronger negative correlation in the second half of the task
(after the self-monitoring prompt).
Methods
This study uses an observational design to analyze the research hypotheses. In a variety
of contrived settings, including on-site at students' schools, as well as clinicians' offices,
nStudy, a web-based software program, was used to engage in direct observation of the tactic
use, as a proxy for SRL of seventh and eighth graders in public and private schools. To ensure
that groups were comparable, reading levels and prior knowledge related to passage content
scores were also measured. A two-part web-based passage about stars adapted from a validated
informal reading inventory was loaded into the nStudy software, which functions as a shell on
top of websites to collect data on traces of participants' use of study tactics. All students were
prompted halfway through the passage to stimulate metacognition about their tactic use.
Students were also asked at the halfway point and at the end of the passage to rate their
enjoyment of the material they were reading using a 10-point slider. Participants were then
administered a criterion-referenced test based on the passage, adapted from the same informal
reading inventory as the passage. Finally, students who used tactics were asked three
qualitative questions about their study experience. In order to answer the research questions
listed above, descriptive statistics and correlations were calculated and where appropriate, the
data were split between the two groups and statistical tests were conducted, including a Mann-
Whitney U, a parametric independent samples and a Wilcoxon Rank-Sum test.

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Results
Students without ADHD used tactics with greater frequency and employed more tactics
overall than students with ADHD, but both groups favored highlighting. Students without
ADHD used the glossary and elaborative strategies more frequently than students with ADHD.
Results of an independent samples t-test and a Mann Whitney U found statistically significant
differences between the non-ADHD group and the ADHD group for both highlighting and
tactics other than highlighting. Quiz scores were comparable, but prior knowledge was related
to quiz score for the non-ADHD group, but not for the ADHD group. Results for tactic use
response to the self-monitoring prompt were not significant for either group; however,
enjoyment in the first half (before the self-monitoring prompt) was significantly positively
related to tactic use in the second half for the ADHD group only. For the non-ADHD group,
enjoyment in the first half was significantly positively related to enjoyment in the second half.
Additional discussion of these results, as well as implications, limitations and
recommendations for future research is provided in Chapter Five.
Organization
This chapter (Chapter One) has provided a brief overview of this dissertation study, its
purposes and its potential contributions. In Chapter Two, a review of the literature on the
following is presented: SRL, issues associated with the measurement of SRL, ADHD,
academic enjoyment and nStudy. The methodology for the dissertation study is presented in
Chapter Three. Results and analyses are discussed in Chapter Four, and finally, the
dissertation concludes with a discussion of the findings of this study, including data about the
differences in SRL between students with and without ADHD. Limitations, implications for
practice, and future research are also included in Chapter Five.
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CHAPTER TWO: LITERATURE REVIEW
This review begins with a discussion of self-regulated learning, including a summary of
the most prominent theories of self-regulated learning, a discussion of the role of self-
monitoring and self-monitoring prompts, and a synthesis of research on learning strategy use.
Where literature was available on middle school students, it is presented; to supplement it,
some literature on the use of strategies among college students is also presented, but
generalizability may be limited, particularly because younger students have demonstrated
weaker self-regulation of their learning (Brown & Smiley, 1978). Since much of this research
has relied on self-reported measures of learning behaviors, the nature and purpose of this study
calls it into question. Next, using Winne and Hadwin’s (1998) four-phase, dual-character
model of self-regulated learning, this review of the literature seeks to identify and to elucidate
the most significant issues and challenges associated with measuring self-regulated learning
and to synthesize existing research on the manifestation of those issues in the administration of
the most commonly used instruments. Literature on ADHD is presented, including descriptive
and diagnostic information, some research on neurological correlates of ADHD and,
information about weaknesses in executive functioning, and a synthesis of the available
literature about the use of learning strategies among students with ADHD. Literature on
academic enjoyment and engagement is discussed, along with available literature on academic
enjoyment among students with ADHD. Finally, background on the nStudy software and a
comment on its potential role in the measurement of self-regulated learning are provided.

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Self-Regulated Learning
Theoretical Foundations of Self-Regulated Learning
Inquiry about metacognition and self-regulation is rooted in the seminal works of
Flavell and Brown (Flavell, 1979), who are credited with the first theories about cognition of
cognition and comprehension monitoring, and Bandura (1986), whose social cognitive theory
introduced self-regulation as self-control of behavior within the reciprocal relationships among
learner, task and environment (Torrano & Torres, 2004; Zimmerman & Martinez-Pons, 1986).
Despite some confusion about the distinctions between metacognition and self-regulation in
learning contexts, for the sake of this review of the literature, metacognition will be defined as
“the awareness and monitoring of one’s comprehension processes” (Mokhtari & Reichard,
2002), and self-regulation as the use of strategies to “plan, organize, self-instruct, and self-
evaluate at various stages” of the learning process (Zimmerman & Martinez-Pons, 1988).
Specifically, Zimmerman and Martinez-Pons’ (1986) definition of a self-regulated learning
strategy as “actions directed at acquiring information or skill that involve agency, purpose
(goals), and instrumentality self-perceptions by a learner” (p. 615) has endured with relative
stability and accord. Metacognition can be conceived as a necessary but insufficient condition
for self-regulated learning, with stronger metacognition positively associated with use of
learning strategies (Vrugt & Oort, 2008). There is also an implication of agency on the part of a
self-regulated learner that exists in contrast to the passivity of a dysregulated learner. Pintrich
(2000) names one assumption of self-regulated learning the “active, constructive assumption,”
because it assumes that learners are “active, constructive meaning makers as they go about
learning” (p. 452). For the sake of further simplicity, self-regulated learning or use of self-
regulated learning strategies will now be referred to as SRL.
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Self-Monitoring and Self-Monitoring Prompts
Zimmerman and Paulsen (1999) characterize self-monitoring as “students’ efforts to
observe themselves as they evaluate information about specific personal processes or actions
that affect their learning and achievement in school,” further noting that “self-monitoring can
serve as a tool for self-improvement by enabling students to direct their attention, to set and
adjust their goals, and to guide their course of learning more effectively (Bandura, 1986;
Corno, 1989, as cited in Zimmerman & Paulsen, 1999, p. 14). The authors go on to discuss the
importance of evaluation and discrimination among different pieces of information. Broadly,
self-monitoring involves discerning the differences among things and making selections based
on those differences. More concretely, self-monitoring allows a learner to take note of their
own comprehension and therefore the effectiveness of the efforts used to achieve that
comprehension. That metacognitive awareness in turn allows the learner to make decisions
and adjustments about the allotment of time and cognitive resources, the strategies used, even
the setting in which the learning is taking place.
In an early study on the role of self-monitoring as feedback to struggling learners,
(Richards, McReynolds, Holt, & Sexton, 1976), However, all the participants in their study
were college students who had expressed concern about their study habits, so results may not
generalize to students who are younger, less aware of their poor study habits, less motivated to
address their concerns, or any combination thereof.
Literature on the effectiveness of self-monitoring prompts, sometimes referred to as
“cues,” has been mixed. An early study (Brown & Smiley, 1978) found prompting to be an
ineffective method to encourage students in several age groups to highlight, and Dinsmore,
Alexander, and Loughlin (2008) question the potential for learners to internalize such
Running Head: NSTUDY FOR ADHD 16

prompting into self-talk (p. 406). As Pressley and Ghatala (1990) suggest, its ineffectiveness
could be related to its execution, consistent with the problems with self-monitoring
Zimmerman identified (described later in this section).
But several recent studies have demonstrated the effectiveness of self-monitoring
prompts on attention, strategy use and learning (Bannert & Reimann, 2011; Kauffman, Zhao,
& Yang, 2011). The work of Kauffman, Zhao and Yang (2011) is particularly relevant to this
study because the authors found that self-monitoring prompts during learning in an online
environment had a significant positive impact on both the notes taken and the learning
outcome. Several other studies (Azevedo & Cromley, 2004 ; Field, Parker, Sawilowsky &
Rowlands, 2010; Rock, 2005) have demonstrated gains from longer-term SRL training as well.
In a two-part study, Bannert and Remann (2011) found positive effects for both a prompting
condition and a prompting condition with training; effects were stronger for their prompting
group that received a 20-minute training.
Specific to students with ADHD, Kofman, Larson and Mostofsky (2008) found
instructional prompting to be far less effective for students with ADHD than their peers. After
explicit instructions in a strategy to be used for a visual discrimination task, 90 % of students in
the non-ADHD group employed the strategy, whereas only 57.5 % of the ADHD group
employed the strategy.
Prior knowledge as Part of SRL
Prior knowledge is a task-specific construct that refers to the related facts, concepts and
procedural knowledge a learner brings to a novel learning situation. The effective use of prior
knowledge when learning new knowledge is a critical process in learning. Students with
stronger prior knowledge tend to perform better on measures of achievement (Greene &
Running Head: NSTUDY FOR ADHD 17

Azevedo, 2009; Moos & Azevedo, 2008). Questions designed to trigger prior knowledge
before learning are often used to improve learners’ connections between old and new
knowledge, as well as their acquisition of new knowledge (Ambrose et al., 2010). Many
researchers (Bannert & Reimann, 2011; Greene & Azevedo, 2009; Leopold & Leutner, 2012)
measure prior knowledge to account for it as an important individual learner characteristic in
their analysis. In a study using a think-aloud protocol, Moos and Azevedo (2007) found prior
knowledge to be significantly positively related to monitoring and planning, but negatively
related to the use of strategies like note-taking and summarizing. They argue that students with
more prior knowledge have developed a “well-established, interconnected knowledge base of
the topic which allows them to . . . regulate their learning by using planning processes to
activate their superior prior domain knowledge and then monitor their knowledge of the topic
by comparing their current knowledge state with information provided in the hypermedia
environment” (p. 289). Indeed, in a study on five predictors of reading comprehension
performance, Cromley and Azevedo (2007b) found that vocabulary and background
knowledge each had medium-sized effects on reading comprehension.
Learning Strategy Use
Findings of research on learning strategy use tend to favor elaborative or generative
strategies. Elaborative strategies involve adding something new to the learning context, like a
question or a new connection in the form of a note, or a category, like the form of a tag or
hashtag. Though the use of these terms in the literature is somewhat inconsistent – and they
are often used interchangeably – generative processing is considered to involve model making
on the part of the learner. According to Wittrock (1992), generative learning involves “four
major processes: (a) attention, (b) motivation, (c) knowledge and preconceptions, and (d)
Running Head: NSTUDY FOR ADHD 18

generation” (p. 531), so a generative strategy would represent a high level of SRL (Van
Blerkom, Van Blerkom, & Bertsch, 2006).
In research studies, elaborative strategies are often contrasted with and found to be
favorable to passive strategies (Bransford et al., 1982; Dunlosky et al., 2013; King, 1992).
Dunlosky et. al grouped categories based on their utility in aiding student learning: elaborative
strategies including self-testing, elaborative interrogation, and self-explanation earned high or
moderate utility assessments, whereas passive strategies including highlighting and re-reading
earned low utility assessments. Cromley and Azevedo (2007a) found that strategy use did not
predict reading comprehension, however, to ensure strategy use, the researchers required ninth-
grade students to select from multiple-choice questions to summarize the material. The
requirement to complete strategy items and the multiple-choice nature of the task did not allow
researchers to determine the attention or mental effort exerted on the strategy selection.
Though published literature on glossary use was not found, K. Rawson (personal
communication, August 6, 2014) surmised that clicking on a glossary would be a passive
strategy because it constitutes exposure to target information rather than elaboration on it.
Learning strategies using spatial organization of information have also been found to
facilitate learning. Kauffman, Zhao and Yang (2011) found that the use of a matrix was
superior to an unstructured note-taking task and a traditional outline format. Leopold and
Leutner (2012) found a drawing strategy to be superior to two text-based strategies,
summarizing and main idea identification, in science learning among tenth-grade students.
Perhaps most relevant to this study, in a very recent study Collazo, Corradi, Elen, and
Clarebout (2014) explored the use and benefit of a concept map in an online environment
among Dutch graduate students. They found that different SRL skills were associated with use
Running Head: NSTUDY FOR ADHD 19

of the concept map. Interestingly, though they found that time using the map was positively
correlated with performance, the quality of the participants’ map completion was negatively
correlated with performance. Similar to this study, the authors collected data on the number of
instances of tool use and learning outcomes measured by a short, mixed-format quiz. However,
application of their study to this one should be limited, as the sample populations are quite
disparate. Furthermore, in exploring the association between SRL and use of online tools, they
relied on self-reports of SRL.
Overall, a more sophisticated profile of self-regulated learning is associated with higher
achievement (Ambrose et al., 2010; Cromley & Azevedo, 2007a; Greene, Moos, Azevedo, &
Winters, 2008; Pressley, Borkowski, & Schneider, 1987), though the causal relationships
remain unclear. However, despite that correlation and despite findings of positive results from
training in learning strategies for (Gajria, Jitendra, Sood, & Sacks, 2007; Lee, Lim, &
Grabowski, 2010), a related body of research aimed at studying students’ reactions and
motivation regarding learning strategies has found resistance to the implementation of
elaborative learning strategies. Dembo and Seli (2004) identified three reasons students in
learning strategies training fail to implement independently the strategies they learn: some
students have negative self-efficacy beliefs about their ability to change their habits, some
students have expectancy value beliefs are outweighed by the perceived effort of implementing
strategies, and some students seem to demonstrate poor executive functioning skills, indicating
they do not know what to change (p. 4). Yuksel (2006) found very similar results among
undergraduates in Turkey who were resistant to a study skills course.
Due to the nature of the need to implement strategies, much of the research on learning
strategies uses students with disabilities as their participants. Corkett, Parrila and Hein (2006)
Running Head: NSTUDY FOR ADHD 20

found that university students with a history of reading difficulties reported using more
organizational strategies than those without reading difficulties. Bell and Limber (2010) found
that low-skill readers tend to rely on more passive strategies than high-skill readers. So while
struggling readers may engage in more strategy use, the strategies they choose are less
elaborative. However, both of these studies relied on self-reports for collection of data on
strategy use.
The Four-Phase Model
Winne and Hadwin’s (1998) four-phase cognitive and behavioral model of SRL for the
activity of “studying,” a generic term to represent goal-directed learning (and, in some practical
contexts, preparation for assessment), depicts four broadly sequential stages of managing
information flow: defining a task, setting goals and planning how to reach them, enacting
tactics, and adapting metacognition. The authors operationalize the term tactic as a micro-pair:
a slice of metacognitive awareness and monitoring, plus an act of metacognitive control.
Together they comprise a tactic, which is an action in response to cognition. They distinguish
between a tactic and a strategy by noting that strategies are more complex and more powerful
(Winne & Perry, 2005, p. 123). The model specifies aspects of SRL the authors deem targets
for measurement: environmental and cognitive conditions, operations (e.g., tactics), internal
products of operations (i.e., information), evaluations of those products, and standards against
which products are monitored (Winne & Hadwin, 1998, p. 280). Most pertinent to this
discussion of measurement, Winne and Perry (2005) posit that if SRL is to be understood and
measured completely, it must be measured from two complementary lenses: as an aptitude,
meaning a relatively stable trait, and as an event, which is essentially a snapshot of behavior in
a given context (p. 534).
Running Head: NSTUDY FOR ADHD 21

Using Winne and Hadwin’s (1998) model of SRL to establish criteria for measurement
tools, this review identifies criticisms about several types of internal validity of measures that
result in an incomplete operationalization of SRL. It addresses further issues of internal
validity inherent in the nature of self-reports, the most commonly used type of measure of
SRL, and finally, it discusses briefly two threats to external validity that result from the
norming process of these instruments.
Issues of Measurement of SRL
Incomplete Operationalization of SRL and Issues of Internal Validity
That strategic learners perform better than passive ones is not typically contested, but
within the discussion of measurement of strategic learners’ cognition and behaviors, data can
be turbid and consensus can be rare. Researchers have used several types of protocols in their
endeavors to capture accurate representations of individual learners’ metacognition, self-
regulation, and use of learning strategies since the mid-1980s, though self-reports have
dominated in both research and clinical settings. These commonly used measures of SRL are
fraught with problems of over-generalization of behaviors as learners report habitual behavior.
Dinsmore, Alexander and Loughlin (2008) conducted a survey of research on measurement of
metacognition and SRL and concluded, citing Winne and Perry (2000) that “Sadly . . . there
remained a strong reliance on self-report and Likert-type instruments and insufficient
corroboration or collaboration of what individuals report they are thinking or doing with actual
traces of such thoughts or behaviors.”
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Winne, Jamieson-Noel and Muis (2002) express concern that current measurements
1

may make faulty interpretations because they measure incomplete representations of SRL.
They lament that no existing measure represents all five targets of SRL, or even an accurate
gauge of any of the Winne and Hadwin’s (1998) four basic phases (see also Butler, 2002; Al-
Hilawani, 2003). The five targets represent a complex feedback loop among task conditions,
cognitive conditions and active aspects of SRL. Omitting any target could cause its under-
representation in measurements and compromise criterion-related validity (Winne et al., 2002),
yet no currently available measure of SRL thoroughly assesses all five targets (Winne et al.,
2002, p. 129; Winne & Perry, 2005). The fact that measures of metacognitive awareness and
SRL account for so little of the variance in scholastic achievement (Sperling, Richmond,
Ramsay, & Klapp, 2012; Tok, Ozgan, & Dos, 2010) could support this claim. Specifically,
current measures do not distinguish very well between deliberate adaptation of tactics over
time and tactic use that is more arbitrary (Butler, 2002; Torrano & Torres, 2004;Winne et al.,
2002), and fail to capture the ongoing improvement in proficiency of strategy use (Cromley &
Azevedo, 2007a). The adaptive development of tactic and strategy use represents arguably all
four, but certainly the last two, phases of Winne and Hadwin’s (1998) model: enacting tactics
and adapting metacognition.
Automaticity of SRL skills presents another issue of construct validity. Even as early as
1979, Flavell (p. 907) conjectured that, while some of these skills and strategies are enacted
deliberately, others become automatized over time, especially for more strategic learners
(Mokhtari & Reichard, 2002, p. 250; Perry & Winne, 2006, p. 215; Torrano & Torres, 2004, p.
5). Not only is measurement of SRL imperfect, but it is also destined to be so, at least without

1
The most commonly used and researched measures include: the Motivated Strategies for Learning
Questionnaire, (MSLQ), the Learning and Strategies Study Inventory (LASSI), the Metacognitive Awareness
Inventory (MAI), and the Metacognitive Awareness of Reading Strategies Inventory (MARSI).
Running Head: NSTUDY FOR ADHD 23

the ability to read and measure individual cognitive moments (Bråten & Samuelstuen, 2007;
Winne & Hadwin, 1998;Winne & Perry, 2005).
In addition to the challenge of assessing all aspects of SRL, there is the ever-present
danger of inadvertently assessing other constructs. Both the SRLIS (Zimmerman & Martinez-
Pons, 1988) and the LASSI (Winne & Perry, 2005) demonstrated discriminant validity by
eliminating potentially confounding factors: achievement test variance unrelated to SRL and
verbal expressiveness (p. 289) on the SRLIS, and items that correlated strongly with social
desirability on the LASSI. Aspects of motivation are inextricably linked to SRL (Flavell,
1979; (Mokhtari & Reichard, 2002); the MSLQ and the LASSI have incorporated motivational
constructs such as goals, value, and expectancy into the measurements, but those constructs
can still confound measurement of SRL (Pintrich, 2004). The MSLQ provides subjects with
descriptions of the constructs being measured, but only after they have responded to items. In
short, if a student exhibits a weakness in an area of SRL, it is difficult, if not impossible, to
judge definitively if that weakness represents a failure of motivation, a cognitive deficiency of
retrieval or working memory, a weakness of reading skill, or one of many aspects of SRL,
including awareness of available strategies (Butler, 2002; Cromley & Azevedo, 2007a;
Pintrich, 2004; Winne & Perry, 2005).
Finally, most currently used measures of SRL do not take learning context into account
(Bråten & Samuelstuen, 2007; Butler, 2002; Hadwin, Winne, Stockley, Nesbit, & Woszczyna,
2012), and instead use broad or generic contexts that may simply assess SRL as an aptitude,
and may even result in inaccurate self-reports (Greene & Azevedo, 2007, p.230; Perry &
Winne, 2006, p.215). Of course learning context would adversely affect all four of Winne and
Hadwin’s (1998) phases; context also relates to the first of the five targets: conditions (Winne
Running Head: NSTUDY FOR ADHD 24

et al., 2002, p. 136). This is especially significant because, when prompted with specific
contexts, students reported varying their tactic use according to the task (Hadwin et al., 2012).
For example, a student might report that he habitually turns headings into questions as part of a
self-test because, as he responds to the question, he thinks of his behavior in his science class,
rather than his English class. Furthermore, students may be more likely to report on contexts in
which they use strategies more effectively because they attribute a positive social value to the
use of such strategies (Wolters, 2004, p. 247). The next section includes a more detailed
discussion of issues associated with self-reports.
The Nature of Self-Reports and Issues of Internal Validity
During the past decade, researchers have questioned several types of internal validity of
self-reports used to measure tactics learners use while studying. They have addressed
construct validity (Perry & Winne, 2006, p. 212; Winne, 2004; Winne & Jamieson-Noel,
2002), convergent validity between the MARSI (which is meant to measure reading strategy
use) and measures of reading comprehension (Cromley & Azevedo, 2007a, p. 232), predictive
validity for younger students (Cromley & Azevedo, 2007a; Quinn, 2011; Rodriguez, Ferreras,
& Izquierdo, 2009), criterion-related validity in the overestimation of retrospective judgments
(Huff & Nietfeld, 2009, p. 164; Winne & Jamieson-Noel, 2002, p. 551) and over-reports of use
of strategies among those who are concerned with appearing competent (Wolters, 2004, p.
247).
Self-reports are inherently vulnerable to criticism of content validity (Cromley &
Azevedo, 2007). Similar to the previous discussion about context, because self-reports ask
students general questions about a course or about habitual behavior, like “I have a purpose in
mind when I read” (Mokhtari & Reichard, 2002), rather than task-specific questions, (Winne et
Running Head: NSTUDY FOR ADHD 25

al., 2002, p. 133). Because they do not measure tangible artifacts of studying, (Bråten &
Samuelstuen, 2007), they may ignore judgments, like ease-of-learning judgments, which is
operationalized as access to prior knowledge during the first phase of learning, defining the
task, and may even ignore judgments about learning during the third phase, enacting tactics
(Winne et al., 2002).
Winne and Perry (2005) claim that assessments of SRL should reflect both aptitude and
event lenses in order to reflect a valid representation of habits (p. 539). Zimmerman and
Martinez-Pons (1986) pioneered efforts to establish a valid and reliable measure of SRL and
faced a lack of established measures of SRL, which would have allowed them to establish
concurrent validity. Their SRLIS, a structured interview protocol that asks students to describe
how they might behave in realistic but hypothetical learning situations, performed well in its
predictions of standardized test achievement, and even its identification of students’ high
school achievement track. However, like other self-reports, it measures SRL-as-aptitude, and
requires a fairly high level of interpretation on the part of scorers (Winne et al., 2002, p. 131),
and therefore may not provide an accurate representation of SRL-as-event (Winne et al., 2002).
Perhaps one of the most significant advances in the identification of problems of
internal validity uses trace methodologies, which are “relatively unobtrusive measurements
representing fossilized strategy events, for example, underlinings in a reading text or
summaries in a notebook existing as concrete remains of bygone episodes of memorization and
organization, respectively” (Bråten & Samuelstuen, 2007, p. 4). Studies have found poor
correlations between self-reports, which represent the aptitude nature of SRL, and traces of
studying, which represent the hitherto elusive measure of SRL-as-event (Hadwin, Nesbit,
Jamieson-Noel, Code, & Winne, 2007; Winne, 2004; Winne & Jamieson-Noel, 2002). These
Running Head: NSTUDY FOR ADHD 26

researchers do not recommend eliminating self-reports, though further research is needed to
determine how the two methods can best complement each other (Perry & Winne, 2006).
Threats to External Validity
Much of the research and some of the instruments, including the MSLQ and the LASSI,
have established reliability and validity only for college students (Greene & Azevedo, 2007;
Mokhtari & Reichard, 2002, p. 250; Quinn, 2011; Reaser, Prevatt, Petscher, & Proctor, 2007),
and so their generalizability, and therefore their external validity, are limited. The MARSI and
Jr. MAI, as well as instruments being developed to measure SRL via trace methodologies, are
focused on school-aged children (Perry & Winne, 2006; H. P. Winne & Hadwin, 2012).
Individual differences of ability, cognition, motivation and experience present a
tremendous challenge to those seeking to establish external validity for instruments that
measure SRL. Students with disabilities, especially with those affecting executive functions,
are likely to struggle with SRL more than other students (Alvarado et al., 2011; Field, Parker,
Sawilowsky, & Rolands, 2010; Lawson, 2009, p. 6), and therefore more likely to be assessed,
more likely to need training of SRL, and by definition more likely to provide inaccurate reports
of their SRL. Yet the norming process of the most commonly used measures of SRL does not
seem to include (or do not deliberately include) students with learning disabilities or ADHD
(Greene & Azevedo, 2007; Reaser et al., 2007). This omission is particularly problematic, as
the measurement of SRL in a clinical setting is most crucial for students with disabilities that
affect their SRL (Alvarado et al., 2011; Field et al., 2010).

Running Head: NSTUDY FOR ADHD 27

ADHD and Executive Functioning
Diagnosis and Prevalence
ADHD is a neurobehavioral disorder with evidence of a genetic basis (Cortese, 2012;
Goldstein, 2010; Tripp & Wickens, 2009) that affects a child’s ability to sustain attention and
inhibit responses. In order to meet diagnostic criteria, a child must demonstrate a “persistent
pattern of inattention and/or hyperactivity-impulsivity that is more frequently displayed and is
more severe than is typically observed in individuals at comparable level of development” in
multiple settings (e.g. home and school) and to such a maladaptive extent as to cause
impairment (American Psychiatric Association, 2000). Updates to the diagnostic criteria
included in the Diagnostic and Statistical Manual (DSM-5) (2013) include a change in the
requirement of the appearance of symptoms from age seven to age 12, and allow students with
an Autism Spectrum Disorder also to receive an ADHD diagnosis if it applies (“DSM-5
ADHD Fact Sheet,” 2013). Categories of items endorsed determine the diagnostic subtype of
ADHD. Students included in this study were diagnosed using the criteria from the DSM-IV,
which included three subtypes: Predominantly Inattentive Type, Predominantly Hyperactive-
Impulsive Type and Combined Type. At least six items of inattention that impair daily
functioning must be endorsed for either Inattentive Type or Combined Type diagnosis.
Examples include “often fails to give close attention to details or makes careless mistakes in
schoolwork, at work, or with other activities,” “often has trouble holding attention on tasks or
play activities,” and “often has trouble organizing tasks and activities.” At least six items of
hyperactivity-impulsivity must be endorsed for either Inattentive Type or Combined Type
diagnosis. Examples include “is often ‘on the go’ or often acts as if ‘driven by a motor,’”
Running Head: NSTUDY FOR ADHD 28

“often talks excessively, “often blurts out answers before questions have been completed,”
“often has difficulty awaiting turn.”
According to the Centers for Disease Control (2013), approximately 11% of children in
the United States aged four to 17 had been diagnosed with ADHD in 2011 and trends were
showing consistent increases in diagnoses over time. Boys are more than twice as likely to be
diagnosed than girls. About half the children diagnosed with ADHD have also been diagnosed
with a behavioral disorder. Additionally, half the children diagnosed with ADHD have also
been diagnosed with at least one comorbid learning disorder; between 30 and 40 % of those
with ADHD are also diagnosed with Reading Disorder (Willcutt, 2014).
Though many questions remain unanswered about the etiology and neurology of
ADHD, research using fMRI to assess volume, blood flow and activity in the brain has
revealed consistent patterns among children and adolescents with ADHD. Furthermore, the
higher-order thinking affected by ADHD is complex, and so it tends to engage several parts of
the brain; indeed Castellanos et al. (2002) found that patients with ADHD had smaller brain
volumes in all regions. Specifically, differences have been found between children with and
without ADHD in the prefrontal cortex (especially the dorsolateral area), which is the area of
the brain that is most associated with higher-order cognitive activity like planning, strategizing,
monitoring, and working memory, the dorsal area of the anterior cingulated cortex, which is
involved in decision-making and the basal ganglia, which is involved in the dopaminergic
pathways that process rewards (Dickstein, Bannon, Castellanos & Milham, 2006; Poissant,
Mendrek & Senhadji, 2014; Schneider, Retz, Coogan, Thome & Rosler, 2006).
Neurocognitive manifestations of those differences between people with and without
ADHD have been well documented. In a fluid reasoning task, Tamm and Juranek (2012) noted
Running Head: NSTUDY FOR ADHD 29

greater activity among children with ADHD than controls in areas implicated in perceptual-
motor coordination and less activity in the prefrontal region, which is the area most commonly
associated with executive functioning and complex reasoning. Neurobiological patterns for
ADHD have been identified both in terms of brain regions, with atypical activity in the
prefrontal cortex, the orbital frontal cortex and the anterior cingulate cortex (Kofler et al.,
2013), and neurochemical pathways, specifically dysfunctions in the dopaminergic, adrenergic,
serotoninergic and cholinergic systems (Cortese, 2012). Patterns in dopamine pathways among
people with ADHD are typically associated with reward circuitry (Sharp, McQuillin, &
Gurling, 2009; Toplak, Jain, & Tannock, 2005; Tripp & Wickens, 2009).
Executive Functioning
It has been widely documented that students with ADHD suffer from deficits in
executive functions (Barkley, 1997; Barkley, 2013; Guare, Dawson, & Guare, 2013).
Executive functions are the cognitive processes associated with carrying out tasks, or as
Castellanos, Sonuga-Barke, Milham and Tannock (2006) refer to them, “a broad range of ‘top-
down’ cognitive processes and abilities that enable flexible, goal-directed behavior” (p. 118).
Executive functions include: attention, inhibitory control, working memory, planning and
implementing strategies. Though patterns of specific executive functions have not been
identified, students with ADHD typically demonstrate deficits in response inhibition, sustained
and divided attention, working memory, motor planning, and fluid reasoning, a type of
problem-solving requiring the integration of several areas of cognition, including attention and
working memory (Pasini, Paloscia, Alessandrelli, Porfirio, & Curatolo, 2007)Tamm &
Juranek, 2012).
Running Head: NSTUDY FOR ADHD 30

These deficits in areas of executive skills present myriad problems for students with
ADHD as they attempt to sustain attention in class, manage their independent time and tackle
multi-faceted assignments and challenging texts. Broadly speaking, they have trouble self-
regulating, including self-directing, self-monitoring, self-restraining and self-assessing.
Behaviorally, even with an Inattentive Type or Combined Type diagnosis, these students have
trouble regulating their attention and engagement to access classroom lectures sufficiently.
When they attempt to tackle complex, long-term assignments, tasks that require extensive
planning and organization, they often feel lost and unsure of where or how to start.
Motivationally, these students also tend to struggle to meet goals. Because of their difficulty
sustaining attention, they often avoid tasks that require high levels of mental effort and
procrastinate (Barkley, 2013). Furthermore, even without a formal diagnosis of a comorbid
Reading Disorder, students with ADHD often demonstrate weaker reading comprehension
skills (Alvarado et al., 2011; Johnson, Reid, & Mason, 2012; Lewandowski, Gathje, Lovett, &
Gordon, 2013).
A Note about ADHD, Hyperactive-Impulsive Type
Many studies on the academic behaviors of students with ADHD include only students
with Inattentive Type and Combined Type. Even several studies that focus on subtype
differences compare only Inattentive Type to Combined Type (Capdevila-Brophy et al., 2012;
Nigg et al., 2002; Nigg, Blaskey, Stawicki, & Sachek, 2004; Pasini et al., 2007; Willcutt,
Doyle, Nigg, Faraone, & Pennington, 2005). It has been suggested that ADHD, Hyperactive
Subtype may have a different etiology and might even be considered a separate disorder
(Barkley, 1997). Willcutt, Pennington and DeFries (2000) suggest that severe hyperactivity
and impulsivity “may be attributable to different etiological influences in individuals with and
Running Head: NSTUDY FOR ADHD 31

without extreme inattention” (p. 149). Pasini, Paloscia, Alessandrelli, Porfirio, and Curatolo
(2007) found that the executive functioning profiles of boys with Inattentive Type and
Combined type were similar, but Gaub and Carlson (1996) found that teachers rated students
with ADHD, Hyperactive Subtype no differently than they did for students without ADHD.
Self-Regulated Learning among Students with ADHD
The problems that Zimmerman and Paulsen (1995) note that interfere with effective
self-monitoring represent precisely the areas of weakness common for students with ADHD.
These problems include: overly optimistic self-efficacy beliefs and the incorporation of those
beliefs into decisions about learning strategy use, inappropriate standards against which
comprehension is measured which result in incorrect feedback during studying, and the
detrimental role of negative affect in response to feedback or outcomes about insufficient
mastery (p. 19). First, a student with ADHD is likely to have trouble with self-assessment
(Barkley, 2013), and therefore may be vulnerable to overestimating his ability to accomplish a
task, particularly without studying. Because students with ADHD tend to use fewer strategies,
use less sophisticated strategies and use strategies less effectively than controls, and because
the first diagnostic criterion for the cluster of inattention in ADHD is “often fails to give close
attention to details” (American Psychiatric Association, 2000), a student with ADHD could
easily measure his progress against an inappropriate standard and thereby provide himself with
incorrect and useless feedback. Finally, because of the frequency of comorbid mood disorder
with ADHD, negative emotional responses to corrective feedback – even if it is feedback that
results from one’s own learning – could certainly derail a student with ADHD.
Students with ADHD are often more impaired academically than their peers (Egeland,
Nordby Johansen, & Ueland, 2010; Loe & Feldman, 2007), and so literature about struggling
Running Head: NSTUDY FOR ADHD 32

students can be generalized to them (with caution). Furthermore, students with weaker content
mastery have more difficulty accurately assessing their abilities (Ambrose et al., 2010), and
due to their difficulty sustaining attention during instruction and the high frequency of
comorbid learning problems, students with ADHD often demonstrate lower levels of
performance.
A few studies have focused specifically on the learning strategy use of students with
ADHD. Some relied on self-reports of strategy use to compare students with and without
ADHD (Lewandowski et al., 2013). Reaser, Prevatt, Petscher and Proctor (2007) found that
among college students, students with ADHD reported weaker information processing and
identification of main ideas and less frequent use of self-testing and test strategies than students
without ADHD (even though some of the non-ADHD group had learning disabilities). Other
researchers (Shallice et al., 2002) have more directly observed that students with ADHD are
less likely to use strategies in learning or testing situations than their peers without ADHD.
Consistent with the notion that students with ADHD may display the problems with self-
monitoring described by Zimmerman and Paulsen (1995), Shiels and Hawk (2010) suggest that
students with ADHD may show weaker error processing skills than typical students. Norwalk,
Norvilitis and MacLean (2008) found that the severity of ADHD symptoms was negatively
correlated with constructs of motivation, academic adjustment, achievement and self-reported
study habits.
In terms of response to intervention, most relevant literature seeks to evaluate longer-
term programs that are not relevant to this study, but some research on minimal training has
shown promising results. Johnson, Reid and Mason (2012) found that high school students
responded positively to training in main idea identification; students also held gains at two- and
Running Head: NSTUDY FOR ADHD 33

four-week follow-ups. Ozdemir (2011) found similar results among seven-year-olds in Turkey
with ADHD after a three-month follow-up.
In contrast, Lewandowski, Gathje, Lovett and Gordon (2013) found no differences
between students with and without ADHD on several measures of reading ability and strategy
use during testing, but they did find significant differences between the two groups in affect,
including anxiety.
Academic Enjoyment
In the literature, academic enjoyment is sometimes represented by the constructs
“engagement” or “interest,” as there is very little recent peer-reviewed research on “academic
enjoyment” or enjoyment specific to a reading or academic task. In some research, interest is
distinguished from but correlated with enjoyment (Thoman, Smith and Silvia, 2011).
Csikszentmihalyi (1991) has published extensively about and “flow” during learning, a state of
engagement characterized by concentration in an activity so focused that it results in a sense of
absolute absorption. In a recent study, Abuhamdeh and Csikszentmihalyi (2014) found that
even for intrinsically motivated people who tend to prefer an optimal level of challenge (e.g. a
close chess game), enjoyment varies widely among activities.
As self-regulated learning is rooted in the triadic relationship among the three
components of social cognitive theory – learner, behavior and environment – the role of
engagement in SRL seems logical. Reed, Schallert and Deithloff (2002) discuss engagement
(referred to as “involvement”) as an antithesis to self-regulation. In fact, she describes a model
in which a successful alternates between self-regulation to determine aspects of a task and
develop a plan, and then a state of involvement, or flow, to accomplish the task (p. 55).
Running Head: NSTUDY FOR ADHD 34

General disengagement has been explored as a growing concern among elementary and
middle school students. Rock (2005) found that all the students she observed were
“chronically disengaged” at least 45 % of their independent seated work time. In their
assessment of the challenges struggling readers in middle school face, Guthrie and Davis
(2003) explain that middle school often represents a time of transition to a stage in their
education that associates joy with reading less than in earlier years. Learners who are
disengaged from reading often struggle with both the reading task and the motivation to read.
They often do not read for enjoyment or associate challenge in reading with enjoyment (p. 60).
While interest and engagement are often thought to be correlated to what educators consider
“good” learning, (Dinsmore et al., 2008; Ely, Ainley, & Pearce, 2013; Thoman, Smith, &
Silvia, 2011), the aforementioned descriptions of involvement and flow seem to preclude a
constant metacognitive internal narrative to monitor and guide a learner’s participation in a
task.
Motivation among Students with ADHD
Students with ADHD often demonstrate motivational difficulties, including lower
levels of persistence and a preference for easy work and less effortful learning strategies
(Barkley, 2013; Carlson, Booth, Shin, & Canu, 2002; Egeland et al., 2010). Research has also
found lower levels of engagement and enjoyment of learning among students with ADHD
(Carlson et al., 2002; Vile Junod, DuPaul, Jitendra, Volpe, & Cleary, 2006). There is also
neurological evidence to support the motivational struggles among students with ADHD.
Reward mechanisms, including dopamine cell firing activity appear to function differently for
people with ADHD (Cortese, 2012; Tripp & Wickens, 2009).
Running Head: NSTUDY FOR ADHD 35

Poor motivational profiles, combined with weaker working memory and processing
speeds typically referred to as the “sluggish cognitive tempo” typical of students with ADHD,
Inattentive Type (Capdevila-Brophy et al., 2012) often constitute a cycle of underachievement.
In a study of children with ADHD aged seven to 11, Lorch et. al (2004) found lack of
engagement to be correlated with comprehension and retention of causal information. The
pattern of weaker or sluggish engagement has also been found in online environments.
(Whirley, Lorch, Lemberger, & Milich, 2003) found that nine- to 11-year-old boys
demonstrated a slower progression of engagement over time during an online story task
compared to their non-referred peers.
nStudy
Based on his model of SRL (Winne et al., 2002), Winne developed a software package
(originally called “gStudy”) to collect data about an individual learner’s tactic use while
interacting with electronic text (Winne, 2004; Winne, Hadwin, & Gress, 2010). nStudy
collects time-stamped trace data that reflect cognitive and metacognitive events in self-
regulated learning (Beaudoin & Winne, 2009; Winne & Hadwin, 2013). Because it is web-
based software, it can be used on any computer with internet access and Mozilla's Firefox
browser.
In response to concerns about the validity and reliability of learner-reported information
on self-reports (Winne, 2010b) and the limited granularity of self-reports of SRL, Perry and
Winne (2006) suggest that nStudy can be used to collect “trace data about how learners select,
monitor, assemble, rehearse, and translate information to learn it” (p. 211). Winne (2010a)
makes the case that the data collected in nStudy can provide a learner with detailed information
about the learning process, which can, in turn, help him become more self-regulated.
Running Head: NSTUDY FOR ADHD 36

nStudy has since been used to collect data about the effects of training in learning
tactics (Winne, 2013), achievement goals (Zhou & Winne, 2012), and the relationship between
achievement goals and the use of tactics. Bernacki, Byrnes and Cromley (2012) found that
higher mastery goals predicted the use of more elaborative tactics.
Summary
In summary, self-regulated learning (SRL) is a complex process operationalized by
Winne and Hadwin (1998) as a four-phase model including: defining a task, setting goals and
planning how to reach them, enacting tactics, and adapting metacognition. A tactic represents
a metacognitive awareness paired with an action in response. The most commonly used
measures of SRL often lack consideration of context and task-specificity, and can under-
represent several components of this model of SRL. The nature of self-reports presents several
problems, including the ability to measure only SRL-as-aptitude, rather than the more real-time
SRL-as-event. Evidence that subjects may not accurately report their strategy use has
questioned their internal validity on several fronts. These instruments also suffer one or both
of two major threats to external validity: a focus on college students in the norming process,
and the exclusion of students with disabilities in the norming process.
ADHD, particularly Inattentive Type and Combined Type, is characterized by
weaknesses in executive functioning, meaning that students with the disorder tend to display
weaker self-monitoring, metacognition, planning, time management and response to feedback.
Little research has been done on the study behaviors of middle school students in the context of
a traditional study-for-a-test task, but extant related literature depicts more limited and less
sophisticated use of learning strategies than their non-disabled peers.
Running Head: NSTUDY FOR ADHD 37

Engagement and enjoyment are typically correlated with achievement, as is SRL, but it
is possible that successful students switch between a self-regulated mode and a “flow” mode,
in which they lose themselves in the accomplishment of a task. Students with ADHD typically
report less enjoyment and are characterized as less engaged than their peers.
Without the ability to read actual thoughts, the automated nature of many aspects of
SRL will continue to relegate measures of SRL to an incomplete representation. However,
Winne and his colleagues’ (2013) online tool measures traces of studying in order to include
aspects of tactic use that may have been thought immeasurable and represents a promising
frontier in measurement of SRL.
Running Head: NSTUDY FOR ADHD 38

CHAPTER THREE: METHODS
In response to a heavy reliance on self-reports of self-regulated learning and concerns
raised about their validity, this study sought a more direct measure of SRL. It also sought to
contribute research on the actual study tactic use among students with ADHD in a traditional
studying context. This study examined the tactic use, response to self-monitoring prompt and
enjoyment among middle school students with and without ADHD in an online environment.
The quasi-experimental study employed an observational design and a developing software
program to collect data. To respond to research questions enumerated in Chapter One,
descriptive statistics and non-parametric analyses were used to compare the number of
instances of tactic use, response to self-monitoring prompt, learning outcome, and enjoyment,
as well as to analyze differences in tactic use and enjoyment before and after the prompt.
Participants
Participants were recruited from students in seventh and eighth grades. Middle school
students were chosen because of the importance of preparation for the text-intensive rigors of
high school, the likelihood of a reliable diagnosis (or lack thereof) of ADHD (“Attention
Deficit/Hyperactivity Disorder,” 2013), and the key developmental processes that occur during
middle school, including the development of the prefrontal cortex, the primary area of the brain
associated with executive functioning.
Word of mouth, publicly accessible online LinkedIn message boards, and CHADD
support groups were used to reach out to clinicians, school administrators and parents of
children with and without ADHD. In order to comply with the protections afforded by
HIPAA, clinicians and school counselors provided contact information to patients who then
opted to initiate participation in the study. Several school administrators were also approached
Running Head: NSTUDY FOR ADHD 39

to offer on-site participation. An independent special needs school in the Los Angeles area
agreed to participate and notified parents of their seventh and eighth grade students of the
study.
Methodology was reviewed and approved by USC’s Institutional Review Board (IRB)
and participating students and parents completed appropriate consent forms to provide
informed consent for theirs and their child's participation in the study (See Appendix A for the
IRB approval letter).
Twenty students participated in the study, 10 without ADHD and 10 with ADHD (see
Table 1 for a representation of demographic variables). Five attend Los Angeles area public
schools, three in the non-ADHD group and two in the ADHD group; the rest attend
independent schools also in the Los Angeles area, where tuition is typically at least $30,000 per
year (Simon, Simon & Dodson, 2013). One parent indicated that the independent school her
daughter attends provides lunch for her daughter; none of the other participants were eligible
for a free lunch at school, though financial aid status is unknown. Sixteen students are
Caucasian, one is Asian and three are mixed race. There were 13 boys and seven girls; all but
one of the students in the ADHD group were boys. Eleven students received training in
learning strategies, eight did not and one parent was not sure. Among the non-ADHD group,
two have learning disabilities, whereas in the ADHD group, seven students have a learning
disability and one parent was not sure.
With regard to the comorbid learning disabilities, multiple diagnoses of learning,
behavioral and other developmental disorders are very common with ADHD (Sharp et al.,
2009), Sherman & Tarnow, 2013. In fact (Pasini et al., 2007)suggest that difficulty with
phonological processing may be a trait of ADHD in boys. Because of these high rates of
Running Head: NSTUDY FOR ADHD 40

comorbidity, it can be very difficult to find a sample of students with “pure” ADHD only;
therefore associated learning problems must be taken into consideration.
As an incentive for participation in the study, parents were offered a summary of their
child's study habits, which offered them a glimpse into what happens behind closed doors
when their child says he or she is studying. As an incentive to participating schools, a parent
talk was offered on a topic of the school’s choice.
To ensure confidentiality, participants were assigned an identification number linked to
their names only on one spreadsheet, which was password-protected and stored on a password-
protected laptop.
Materials
Qualitative Reading Inventory-5
The Qualitative Reading Inventory-5 (QRI-5) (2011) is an instrument designed to
assess a student's reading fluency, comprehension, and immediate recall of information, using
both expository and narrative texts. Informal reading inventories such as the QRI-5 are often
used by school psychologists, educational therapists and special education teachers to measure
a student's progress toward reading goals. This instrument was selected because of its
acceptance in the community of educational diagnosticians, its ease of use, its reliability and
validity data, and the fact that the QRI-5 includes four pieces used in this study: a reading
fluency measure, prior knowledge questions for each passage, passages that have been assessed
to establish grade level, and comprehension questions.
Reading fluency. Because some students, regardless of strength of executive
functioning, may naturally be much stronger readers, and students with ADHD often have co-
morbid reading problems (Willcutt et al., 2000), reading fluency will be measured and used as
Running Head: NSTUDY FOR ADHD 41

a proxy for reading ability. Oral reading speed has been validated as a measure of reading
ability (Fuchs & Fuchs, 1999; Fuchs, Fuchs & Maxwell, 1988; Kame'enui & Simmons, 2001,
as cited in (Leslie & Caldwell, 2010) and will allow comparison of the two groups in their
reading ability.
The QRI-5 includes several measures of reading fluency. The first measure used for
this study was a sequence of lists of words out of context (see Appendix B for the student
version of the word lists administered). First, students were asked to read the “Upper Middle
School” list; then, if they did not earn an “independent level” score, they was asked to read the
next lowest list (see Appendix C for the examiner version of the word lists). This procedure
was continued for up to four lists, but students were not told the levels of the lists.
Next, the Upper Middle School passage "Malcolm X" was used for reading fluency
because it was leveled at grade 7.2, meaning that the mean of three readability formula
estimates (the Dale-Chall formula, the Fry Graph and the Flesch Grade Level) represented
early seventh grade (Leslie & Caldwell, 2011, p. 469). This passage was also selected because
it includes a range of very natural language and some nonsensical quotations that required
attention to detail. It has both face validity and empirical validation. Due to time constraints,
only the first 269 words of the passage were administered (see Appendix D for the section of
the passage that was administered).
Prior knowledge. Because pilots of the QRI-5 passages have revealed significant
correlations between prior knowledge and comprehension questions with an average or r = .57,
it is important to control for it. The prior knowledge assessment (see Appendix E for the
assessment) consists of broad concept questions included in the QRI-5. Prior knowledge
questions for this study include "What are stars made of?" and "What do nuclear reactions
Running Head: NSTUDY FOR ADHD 42

produce?" The QRI-5 includes a scoring rubric for the prior knowledge concept questions and
the prediction task; responses were scored on a three-point scale. For the concept questions, a
three-point response includes a "precise definition, a definitional response to a phrase, or an
answer to a question specifically related to passage content;" a two-point response includes
only an example, specific attribute or function of the concept; a one-point response reflects a
general or personal association with the concept; and a zero-point response reflects an
unrelated response or an "I don't know" response (Leslie & Caldwell, 2001, p. 49). The
questions that apply to the passage carry a total of 12 possible prior knowledge points.
Passages. The passage selected for this study, "Life Cycles of Stars – Part 1” (re-
named “Star Formation" for clarity) was rated at the 7.5 grade level. It consists of eight short
paragraphs about how stars are created (see Appendix F for both sections of the passage about
stars). It was modified slightly to correct mechanical errors and to improve clarity. Technical
scientific subject matter was selected in order to provide a sufficient demand of attention on the
part of the reader.
Reading Comprehension Questions. A criterion-referenced assessment of
comprehension of the passage was derived from the QRI-5's reading comprehension questions,
which have been analyzed to ensure reliability and validity (see Appendix G for the derived
assessment). Cronbach's alpha for inter-scorer reliability for comprehension questions was .98.
The QRI-5's assessment consists of 10 open-ended explicit questions, such as "How long is the
life cycle of the sun?" and implicit questions like "If a protostar doesn’t give off light, how do
scientists know it exists?" Authors of the QRI-5 (2011) found that the mean proportion of
correct scores on comprehension questions was .58 with a standard deviation of .15 for the
passage. The derived assessment includes several of those questions verbatim, and to facilitate
Running Head: NSTUDY FOR ADHD 43

scoring, modified some of the questions into a multiple-choice format. This balance was
struck in order to optimize reliability of scoring, as well as valid measurement of a student's
learning outcome.
The Vanderbilt ADHD Diagnostic Parent Rating Scale
The Vanderbilt ADHD Diagnostic Parent Rating Scale (VADPRS) (See Appendix H
for the original instrument) is a widely accepted behavior rating checklist typically used in the
diagnostic process for ADHD. Parents report their child's behaviors according to the frequency
Code: 0 = never; 1 = occasionally; 2 = often 3 = very often. Items include behaviors associated
with the DSM-IV diagnostic features of ADHD, such as "Does not follow through on
instructions and fails to finish schoolwork (not due to oppositional behavior or failure to
understand)" and "Is easily distracted by noises or other stimuli." This instrument was
developed from the criteria for ADHD diagnosis as published in the DSM-IV, using the 95th
%ile as the cutoff for clinical significance. Wolraich (2003) found whole scale internal
consistency reliabilities to have overall Cronbach's alpha ≥.90 in all samples. Collett et al
(2003) found an internal consistency reliability of 0.94–0.95 and concurrent validity with the
teacher scale to be .79.
Procedures
The nStudy software used to collect traces of tactic use is portable, so the study was
administered in an office in a one-on-one setting with minimal distractions. Data collection
was done in the researcher’s educational therapy office, at a psychologist’s office or in an
office at the school site. All three settings were appropriate for test administration in terms of
furniture, lighting, temperature, noise and privacy.
Running Head: NSTUDY FOR ADHD 44

The web-based software program nStudy was used to collect data (Perry & Winne,
2006). The program, which was developed by Winne at Simon Fraser University, creates an
online learning environment that offers a collection of tools that students employ in order to
enact various learning tactics to regulate their learning. For this study, nStudy was configured
to allow participants to enact five learning tactics: highlighting, tagging,
2
note-taking, web-
browsing, and glossary searching. Highlighting simply refers to electronic selection of text.
Tagging refers to selecting text and assigning to it one of an existing collection of short
phrases, tags. For example, a participant might highlight a sentence about the protostar phase
and tag it "protostar." Note-taking refers to a participant's capability of selecting text and
typing a note about it in any form (e.g. bullets, sentences, two groups of bullets). In this study,
glossary searching refers to the capability to click on a vocabulary word or term (denoted by
purple underlined text) and link to a list of words and definitions in alphabetical order.
Because this capability was unavailable in nStudy at the time of study design, an additional
glossary page was created using 10 words and terms from the passage. Examples of words and
terms included in the glossary are “life cycle,” “infrared” and “matter.” Definitions were
retrieved from two online dictionaries designed for children (“Word Central,” n.d.; Wordsmyth
Organization, n.d.). Web-browsing refers to the capability to click on a web page while
reading in order to gain additional information about a word or idea.
Prior Knowledge
Because prior knowledge is often predictive of performance on curricular tests (Leslie
& Caldwell, 2010), before SRL data collection, students completed an out-loud assessment of

2
Due to a technical issue related to the development of the nStudy software, selection of pre-loaded tags was not
available. In lieu of this functionality, participants were provided with a list of suggested tags on paper.
Running Head: NSTUDY FOR ADHD 45

their prior knowledge about stars; their responses were inputted into an online survey (see
Appendix E for the questions).
Reading Fluency
For this study, a measure was need that would help to determine either that students
with and without ADHD are reading at a comparable level, or if they are not, the differences in
mean reading ability. Two methods were used: words in isolation to indicate grade level of
reading, and out-loud passage reading of a grade-level passage to track the total number of oral
reading miscues, as well as the number of meaning-changing miscues.
nStudy Training and Instructions
In order to minimize cognitive load, participants were first provided with a short
training in user procedures for nStudy via web-based training pages using simple, two-
paragraph passage about cars for each tactic (see Appendix I for the training pages that were
used). They were also provided with a Quick Reference Guide (see Appendix J for the guide).
During the training, participants were supervised as they enacted each tactic in a structured task
based on sample text. Instructions were provided in red letters on the training pages and read
aloud.
Administration procedures also included instructing students: (a) that they were to read
and study a passage about stars in two parts; (b) that there would be a short-answer test when
they were done; (c) that they would have as much time as they need to read, would then have
up to five minutes to study their highlights, and notes, but would not be able to review the
passage itself; (d) that they are allowed to use any of the nStudy tactics they just learned; and
(e) that they should "study the way they would for a test they really cared about."
Running Head: NSTUDY FOR ADHD 46

After participants completed prior knowledge questions and the training pages, they
moved to an nStudy page in the Firefox web browser and read "Star Formation - Part 1," the
first part of an expository, middle school-level passage in the QRI-5.
Self-Monitoring and Enjoyment Prompts
When students completed the first section, they encountered an interstitial page, which
provided two prompts. The first prompt was the question, "How much are you enjoying the
material you are reading about stars?" followed by a request to rate the participant's level of
enjoyment using a 10-point (though the points were invisible) slider that ranges from "Do not
enjoy at all" to "Enjoy a lot." The second prompt is the question: "Do you remember the five
study tools you were shown how to use before you started the passage?" followed by a list of
the five tactics, followed by the prompt: "Have you been using them? Don't forget, you have as
much time as you need to read and use tools to prepare for the test at the end." These self-
monitoring prompts will encourage participants to evaluate the effectiveness of their current
use of tactics and improve their regulation of learning, if necessary (Kauffman et al., 2011).
Next, students read the second section of the passage. When participants completed
the second section, they again encountered an interstitial page that asked them to rate their
level of interest. It also provided the following prompt: "You may now review for your test for
up to five minutes if you would like. You may review the passage, as well as your highlights,
notes and tags, but you may not make any new ones. We will let you know when five minutes
have passed." (See Appendix K for the web pages used to prompt participants.)
Quiz and Questions
When a participant decided he or she was ready, or when five minutes had passed, the
criterion-referenced assessment developed from the QRI's reading comprehension section was
Running Head: NSTUDY FOR ADHD 47

administered. Students who used tactics were asked three follow-up questions. First, they were
asked which strategy was most useful to them. Second, they were asked if their study behavior
on this task was a good representation of how they normally study. Third, students were asked
if they would use the nStudy software if it were available to them.
Parent Survey
Finally, parents completed an online survey (see Appendix L for the parent survey) that
included basic demographic information as well as the inattentive and hyperactive symptom
sections of the VADPRS to confirm the appropriateness of the ADHD diagnosis.
Demographic data collected include: age, type of school (e.g. public, independent), race or
ethnicity, gender, language primarily spoken at home, and free lunch eligibility. Gathering
these data will allow me to ensure that there are no significant differences between the group of
students with ADHD and without in realms that might affect self-regulated learning or reading
ability. Parents were also asked to report whether their children had received training in
learning strategies (which often indicates support in the form of educational therapy, a
treatment plan designed to improve the study behaviors among students with related deficits).
Data Analysis
Tactic Use
nStudy generates web analytics that represent the number of occurrences of use of each
tactic for each student. nStudy captures quantitative information about tactic used as well as
qualitative information, such as the specific text that participants highlight and the text of the
notes they create.

Running Head: NSTUDY FOR ADHD 48

Scoring the QRI-5
Word lists. The words on the “Upper Middle School” level word list were taken
directly from the passage used to measure oral reading miscues of text, so the word list must be
administered before the passage and scored in real time. Each list has 20 words on it. The lists
were scored simply by tallying the total number of words identified correctly. According to
the QRI-5, if a student pronounces 90%-100% of the list correctly, the list is at the
“independent” level for him. Seventy to 85 % represents the “instructional” level and below
70 % represents the “frustration” level (Leslie & Caldwell, 2011, p. 44). The QRI-5 allows an
examiner to differentiate between words that a student identifies immediately from those he or
she must sound out. Though the difference between words read within one second and words
that took more time was recorded, for the purposes of this study, all words that were
pronounced correctly were counted toward the student’s reading fluency score. This decision
was made partly for ease of analysis and partly because students with ADHD with inattentive
symptoms typically have slower visual processing speeds (Capdevila-Brophy et al., 2012) than
other children.
Oral passage reading. Oral reading of text is scored by tracking the number of
miscues a student makes. Miscues include substitutions, omissions, insertions, self-
corrections, reversals, and punctuation ignored. The QRI-5 provides guidelines for scoring
both total miscues and meaning-changing miscues. A meaning-changing miscue is “any
deviation from the text that results in an ungrammatical sentence or in a grammatical sentence
that differs from the author’s meaning” (Leslie & Caldwell, 2011, p. 63).
Prior knowledge. The QRI-5 also provides guidelines for scoring responses to the
prior knowledge questions. Each questions is worth three points. A three-point answer is “a
Running Head: NSTUDY FOR ADHD 49

precise definition, or a definitional response to a phrase, or an answer to a question specifically
related to passage content” or a synonym. A two-point answer is “an example of a concept,”
“a specific attribute or defining characteristics” or a function. A one-point answer is “a general
association,” “isolation of a prefix, suffix or root word, or “firsthand, personal associations.”
No points are scored for unrelated responses (Leslie & Caldwell, 2011, pp. 49-51).
Reading comprehension. The QRI-5 also provides guidelines for scoring the
comprehension questions. Responses to explicit questions must come from the passage and
responses to implicit questions must relate to a clue in the passage. (Leslie & Caldwell, 2011,
pp. 75-76). Two coders independently scored the open-ended questions (ICC = .88).
Quantitative Analysis
To answer the research questions, descriptive data were compiled and means, standard
deviations and correlations were calculated. Where appropriate, t-tests and non-parametric
alternatives were run. Information about responses to qualitative questions is summarized. A
detailed description of data analysis appears in Chapter Four.

Running Head: NSTUDY FOR ADHD 50

CHAPTER FOUR: RESULTS
Included in this chapter are demographic information, descriptive data about reading
ability and prior knowledge, and analyses of the results for each of the research questions. To
depict a profile of the students included in the study, demographic information in the form of
frequencies in each category are described. Next, descriptive data about reading fluency,
including means for the ADHD and non-ADHD group, are provided to demonstrate the
similarities and differences between the two groups in the students’ reading ability. For the
remainder of the chapter, each research question and hypothesis is addressed with both
descriptive data and quantitative analysis. An a level of .05 was used for all statistical analysis.
The initial goal of the study was to establish self-regulated learning profiles of students
with and without ADHD. Though missing data were minimal, and information was gathered
for as many as 20 variables in addition to information about executive functioning weaknesses,
due to the smaller than anticipated sample size, data analysis will be limited to that of “tactic
use” rather than “self-regulated learning,” though tactic use should serve as a proxy for an
individual’s SRL. (See Tables 2 and 3 for parametric correlation matrices for variables of
interest in the non-ADHD and ADHD groups and Tables 4 and 5 for non-parametric matrices.)
To answer the first two research questions, “Does study tactic use differ between
students with and without ADHD when studying for a test in an online learning
environment?” and “What specific behaviors characterize the process of studying for students
with and without ADHD?” an independent-samples t-test and a non-parametric Mann-Whitney
U were run to explore differences between the two groups in both their total tactic use and their
use of each of the five available tactics. To answer the question “Do self-monitoring prompts
during studying affect the use of study tactics differently between the two student
Running Head: NSTUDY FOR ADHD 51

groups?” Wilcoxon Rank-Sum tests were run as non-parametric alternatives to two-tailed,
paired-sample t-tests for the ADHD and the non-ADHD group to determine whether the
students’ behavior was significantly different before and after they were given a prompt to use
tactics. To answer the question, “Is tactic use associated with learning outcomes, as measured
by the criterion-referenced quiz?” the data were split into the two groups – ADHD and non-
ADHD – and correlations were examined to determine whether students’ use of tactics was
related to their performance on the quiz. To answer the question, “Is self-reported enjoyment
before and after the prompt associated with tactic use for one or both of the groups?” the data
remained split between the two groups and correlations were examined to determine whether
students’ use of tactics affected their reported enjoyment of the material. Since enjoyment data
were collected after the first half of the passage (before the self-monitoring prompt) and after
the second half of the passage (after the self-monitoring prompt), associations among students’
tactic use and enjoyment before and after the self-monitoring prompt were also examined.
Due to the small sample size, linear regressions using all the covariate data collected
were not appropriate. Specifically, the sample was too small to attain power to account for
several measures of reading fluency, prior knowledge and grade level in school. For variables
whose means were similar between the ADHD group and the non-ADHD group, correlations
were compared without using a multivariate regression. Note that for many of the results of
statistical analysis, standard deviations are quite large, likely because of the small number of
participants in each group.
Demographic Variables
As discussed in Chapter Three, the sample included 20 students, 10 with ADHD and 10
without. Demographic data were collected from parents of all 20 students. All students were
Running Head: NSTUDY FOR ADHD 52

in seventh or eighth grade and between the ages of 12 and 14. There were thirteen boys and
seven girls, though all but one of the students in the ADHD group were boys. Sixteen students
are Caucasian, one is Asian and three are of mixed race. Five attend public schools and 15
attend independent schools. None of the participants were eligible for a free lunch at school.
Within the non-ADHD group, two have learning disabilities, while at least seven of the ADHD
group also have comorbid learning issues (one parent was not sure). Four students without
ADHD and seven students with ADHD have received training in learning strategies. (See
Table 1 below for information about demographic variables).
Ratings of Symptoms of ADHD on the VADPRS
None of the parents of students in the non-ADHD group endorsed enough items to
support an ADHD diagnosis by the VADPRS checklist (see Appendix L for the parent survey
including items taken from the VADPRS). Parents of eight of the ten students in the ADHD
group endorsed enough symptoms in the Inattention domain to confirm their ADHD diagnosis
in either the Inattentive Type or Combined Type. The two remaining students had received
ADHD, Hyperactive type diagnoses and were also in the ADHD group. (See Chapter Five for
a discussion about this decision.)
Reading Ability
Reading fluency measures were collected and used as proxies for reading ability; means
between the two groups were compared to determine whether reading level could be eliminated
as a covariate. Three measures were collected: independent reading level measured by
performance on leveled word lists out of context from the QRI-5, total oral reading miscues,
and meaning-changing miscues determined by guidelines in the QRI-5 (Leslie & Caldwell,
2011, pp. 60-69). For some students, an instructional reading level was also collected.
Running Head: NSTUDY FOR ADHD 53

Independent reading grade levels were comparable between the two groups: students without
ADHD scored a mean grade level of 6.40 (SD = .070) and students with ADHD scored a mean
grade level of 6.22 (SD = .69). However, though the grade level for both groups was
comparable, differences started to emerge with the way students in each group handled specific
words, as students ADHD began to demonstrate more trouble with oral reading. Specifically,
students with ADHD made more miscues on an oral passage reading task (M = 13.11, SD =
4.86) than those without ADHD (M = 7.80, SD = 4.80). Students with ADHD also made more
meaning-changing miscues (M = 8.22, SD = 3.58) than students without ADHD (M = 4.80, SD
= 2.86).
Prior Knowledge
Information about the students’ prior knowledge about relevant information about stars
was collected. The four open-ended prior knowledge questions included in the QRI-5 were
included and scored according to the guidelines in the QRI-5 (Leslie & Caldwell, 2011, pp. 48-
53); means between the two groups were compared to determine whether reading level could
be eliminated as a covariate. Prior knowledge scores were comparable between the two
groups, though the students without ADHD slightly higher: students without ADHD earned a
mean score of 7.80 (SD = 3.39) and students with ADHD earned a mean score of 7.00 (SD =
3.27).
Study Tactic Use among Students in Both Groups
The hypotheses for the first two research questions, “Does study tactic use differ
between students with and without ADHD when studying for a test in an online learning
environment?” and “What specific behaviors characterize the process of studying for students
with and without ADHD?” predicted that students with ADHD would use fewer tactics and
Running Head: NSTUDY FOR ADHD 54

would be more likely to engage in passive strategies like highlighting and clicking on the
glossary, whereas students without ADHD would be more likely to engage in more elaborative
tactics (creating notes and tags and seeking supplemental information from websites).
Data were collected for all participants, including 10 without ADHD and 10 with
ADHD. All students without ADHD used tactics; the range was from 5 to 30 tactics, with a
mean total tactic use of 13.30 tactics (SD = 7.48). Though there were no outliers per se, this
standard deviation represents the large range of tactic use among students without ADHD,
which resulted in a great deal of variability in tactic use. Among students with ADHD, three
students used no tactics at all; the range of tactic use was from 0 to 4. The mean total tactic use
among students with ADHD was 1.5 (SD = 1.55).
It should be noted that the two students who were included in the ADHD group because
of their formal ADHD diagnoses but whose parents did not endorse a sufficient number of
items on the Vanderbilt to indicate ADHD were two of three students in the ADHD group (n =
10) who used more than one tactic. One used three and the other used four. Both of these
students received a diagnosis of ADHD – Hyperactive Type, meaning that their profiles reflect
a pattern of hyperactivity and impulsivity, but not necessarily a pattern of inattention
(American Psychiatric Association, 2000; Willcutt, 2014).
Highlighting comprised the majority of the tactic use in both groups: in the non-ADHD
group it accounted for 90 instances of tactic use out of a total group tactic use of 133 and in the
ADHD group it accounted for 12 instances out of a total group tactic use of 15. Nine out of 10
students without ADHD engaged in some highlighting, with a range of 0 to 22, whereas four
out of 10 students with ADHD engaged in highlighting with a range of 0 to 4 (see Table 6 for a
breakdown of tactic use). Because of both groups’ heavy use of one tactic, the Total Tactic
Running Head: NSTUDY FOR ADHD 55

Use category was broken into two categories: Highlighting and an aggregate of all tactic use
other than highlighting (creating notes, creating tags, visiting the glossary and visiting other
websites) called “Other than Highlighting.”
In order to test the hypothesis, means for the two groups were compared using both
parametric and non-parametric tests. The non-parametric Mann-Whitney U was used and, to
confirm results, a parametric independent-samples t-test was used to measure the effect of
membership in the ADHD or non-ADHD group on tactic use.
Total Tactic Use met the assumption of normality for both the non-ADHD (p = .609)
and the ADHD group (p = .201) according to a Shapiro-Wilk test. For the non-ADHD group
Highlighting (p = .071) and Other than Highlighting (p = .124) did meet the assumption of
normality, but they did not for the ADHD group. Given that the ADHD group total for Other
than Highlighting was three tactics, this characteristic is not surprising.
Results of both parametric and non-parametric tests confirmed the first part of the
hypothesis, as students without ADHD used many more tactics than students with ADHD.
Results of Mann-Whitney U tests using Total Tactic Use (U = .000) as the dependent variables
were significant for differences between the non-ADHD (M = 13.30, SD = 7.48) and the
ADHD (M = 1.5, SD = 1.55) groups. Results of Mann-Whitney U tests using Highlighting (U
= .001) as the dependent variable were significant for differences between the non-ADHD (M
= 9.00, SD = 6.29) and the ADHD (M = 1.20, SD = 1.85) groups.
Confirmatory post-hoc data were run using a parametric independent-samples t-test
with identical results. Effect sizes for Total Tactic Use (d = 1.96) and Highlighting (d = 1.3)
were large.
Running Head: NSTUDY FOR ADHD 56

Results partially confirmed the second part of the hypothesis, as students without
ADHD used more of the non-highlighting tactics than students with ADHD. All students in
the non-ADHD group used at least one tactic other than highlighting. However, the two
groups’ use of the glossary did not support the categorization of glossary use as a passive tactic
and the hypothesis that students with ADHD would be more likely to use it. In fact, eight out
of 10 students without ADHD clicked on the glossary a total of 16 times, whereas in the
ADHD group, one student clicked once on the glossary.
Use for the remaining tactics was as follows: in the non-ADHD group, six students
created a total of 15 notes, three students created a total of 10 tags,
3
and two students each
visited one website. In the ADHD group, none of the students created any notes, one student
created a tag, and one student visited another website (see Table 6 and Figure 1 for visual
representations of tactic use data).
Results of a Mann-Whitney U using Other than Highlighting as the dependent variable
was significant (U = .000) for differences between the non-ADHD (M = 4.3, SD = 3.68) and
the ADHD (M = 0.3, SD = .52) groups. Confirmatory post-hoc data were run using a
parametric independent-samples t-test with identical results. Effect sizes for Other than
Highlighting was medium (d = .66).
Previous training in learning did not seem to have an impact on tactic use in this study
for either group. All three of the students who used no tactics at all were in the ADHD group,
and all of their parents reported that they have had training in learning strategies. Parents of
only two of the students with ADHD reported that their children have not had specific training
in learning strategies; one used one tag and the other used four highlights. Among the non-

3
Due to the in-development status of the software, selecting tags was not available, so students were given a post-
it note with suggested tags that they typed in.
Running Head: NSTUDY FOR ADHD 57

ADHD group, four of the students’ parents reported that they have received training in learning
strategies though their tactic use did not differ from others without ADHD who did not receive
training.
Students who used tactics were asked three follow-up questions (see Table 7 for a
summary of responses to follow-up questions). First, they were asked which strategy was most
useful to them. In the non-ADHD group (n = 8), six students reported that highlighting was
the most useful strategy. For all of these students, highlighting did, in fact, represent at least
70% of their tactic use. Two students in the non-ADHD group reported preferring tactics other
than highlighting: one preferred notes and the other preferred the glossary. Again, their tactic
use supported their preferences.
In the ADHD group, of those students who used tactics (n = 7), the two students who
reported that highlighting was most useful each used four highlights and no other tactics, so
highlighting represented 100% of their tactic use. However, among students with ADHD, one
student with ADHD who used the highlighting tool three times reported that he did not prefer
any tactics. Of the four students who reported preferring a tactic other than highlighting, two
reported preferring notes, though neither of them created any notes.
Second, students were asked if their study behavior on this task was a good
representation of how they normally study. In the non-ADHD group (n = 8), five students
reported that their study tactic use in this study was a good representation of how they
normally study and one said it depended on the circumstance. One student reported that she
used self-testing and the other reported that he writes definitions and takes notes, rather than
quotes directly from text. In the ADHD group, of the students who used tactics (n = 7), two
Running Head: NSTUDY FOR ADHD 58

reported that their behavior in the study represented their normal study behavior, three said it
depended on the circumstances, and two said it did not.
Finally, students were asked if they would use the nStudy software if it were available
to them. In the non-ADHD group (n = 8), five said they would, two said it depended on the
circumstances and one said he would not. In the ADHD group (n = 7), four said they would,
and three said it would depend on the circumstances.
Self-Monitoring Prompts
The hypothesis for the research question, “Do self-monitoring prompts during studying
affect the use of study tactics differently between the two student groups?” predicted that all
students would increase their study tactic use after the self-monitoring prompt, but that
students without ADHD would respond with a larger increase.
Results of a Shapiro-Wilk test for normality showed that for the non-ADHD group,
Tactic Use – First Half and Tactic Use – Second Half were normally distributed, but for the
ADHD group, only Tactic Use – First Half was normally distributed.
To determine whether either group demonstrated a significant response to the self-
monitoring prompt, a non-parametric, two-tailed Wilcoxon Rank-Sum test was run for each
group, comparing their study tactic use before the prompt to that after the prompt. Results were
not significant for differences before and after the prompt for either group.
Because tactic use before and after the prompt were normally distributed for the non-
ADHD group, a two-tailed, paired-sample t-test was also run for this group. Results trended
toward significance for this group: t (9) = -1.959, p = .082.
For the ADHD group, tactic use in the second half, after the self-monitoring prompt,
may have failed tests of normality at least partly due to those students’ limited use of tactics in
Running Head: NSTUDY FOR ADHD 59

the second half (five students used none and four students used one). The t-test and Wilcoxon
may also have failed to result in significant results in part because of the small sample size. So
to explore these data further, a simple differential between tactic use in the first half and tactic
use in the second half was calculated to explore the difference in the two groups’ responses to
the prompt. The differential represents the difference between tactic use before and after the
self-monitoring prompt (which reminded students of the tactics that were available to them),
with a positive number indicating an increase in tactic use and a negative number indicating a
decrease in tactic use. Though there was no significant difference between the two groups in
their response to the self-monitoring prompt per the t-test, the lack of response to the prompt
within the ADHD group is striking. The mean differential in the non-ADHD group was 2.30
(SD = 3.71), meaning that on average, students without ADHD used 2.3 more tactics in the
second half of the passage after the prompt than they did in the first half. Within this group,
seven out of 10 students increased their tactic use after the prompt, while three decreased tactic
use after the prompt.
In contrast, the mean differential in the ADHD group was -0.30 (SD = 1.06), indicating
an average decrease of tactic use after the prompt. In fact, of the seven students with ADHD
who employed tactics, four of them used fewer after the prompt, one used the same number in
both halves and two increased their tactic use (see Figure 2 for a graph depicting students’
responses to the prompt in both groups).
Learning Outcome
The hypothesis for the research question, “Is tactic use associated with learning
outcomes, as measured by the criterion-referenced quiz?” predicted that tactic use for the two
groups would be correlated with performance on the quiz. To determine this association, the
Running Head: NSTUDY FOR ADHD 60

data were split into two groups to allow for analysis of the students with and without ADHD
separately. Means were calculated and correlations between tactic use and quiz score and then
other variables and quiz score were examined within each group.
Results of Shapiro-Wilk tests of normality showed that Total Tactic Use, Tactic Use –
First Half, Meaning-changing Miscues and Quiz Score were normally distributed for both
groups of students. The variables Prior Knowledge, Other Than Highlighting and Tactic Use –
Second Half were normally distributed for the non-ADHD group only. Again students with
ADHD demonstrated very limited use in both the Other Than Highlighting and Tactic Use –
Second Half categories, so again those variables are not normally distributed for students with
ADHD.
Tactic Use
Despite the differences in tactic use between the two groups, mean quiz scores were
similar among students with and without ADHD. The mean quiz score for the non-ADHD
group was 7.50 (SD = 2.22) and the mean score for the ADHD group was 7.00 (SD = 2.48).
Non-parametric correlations using Spearman’s rho, as well as parametric correlations using
Pearson’s r, indicated that Total Tactic Use and Other than Highlighting were not significantly
associated with performance on the quiz for either the non-ADHD or the ADHD group.
Variables Other than Tactic Use
Correlations did show that for the non-ADHD group only, students’ prior knowledge
about stars was significantly associated with performance on the quiz with a Pearson product-
moment correlation of .884 (p = .001); prior knowledge therefore accounted for about 78% of
the variance in quiz score. However in the ADHD group, both parametric and non-parametric
correlations showed no significant association between prior knowledge and performance on
Running Head: NSTUDY FOR ADHD 61

the quiz. So despite the similar prior knowledge scores, prior knowledge scores predicted
learning for students without ADHD, but did not for students with ADHD (see Figure 3 for a
scatterplot).
However in the ADHD group, Quiz Score appears to be related to reading ability. In
the ADHD group, Quiz Score was significantly inversely related to Meaning-changing
Miscues (r = -.687, p = .041), a finer-grain variable to measure reading fluency than grade
level: it captures the number of instances in which a student makes an omission or commission
that changes the meaning of the sentence he or she is reading out loud. Also for the ADHD
group only, the correlation between Quiz Score and Instructional Fluency approached
significance (rho = -.718, p = .069)
4
. No measures of reading fluency were correlated to Quiz
performance for the non-ADHD group.
Enjoyment
The hypothesis for the research question, “Is self-reported enjoyment before and after
the prompt associated with tactic use for one or both of the groups?” predicted a positive
correlation for students without ADHD and a negative correlation for students with ADHD.
Means were calculated and correlations between variables were examined within each group.
Findings, did not support the hypothesis, as enjoyment appears to be related to tactic use for
the ADHD group but not for the non-ADHD group.
Results of a Shapiro-Wilk test of normality revealed that Enjoyment after First Half and
Enjoyment after Second Half were normally distributed for both the ADHD and non-ADHD
groups.

4
As most students were either reading at the seventh- or eighth-grade level, Independent Fluency Grade Level
(GL) and Instructional Fluency GL were not normally distributed for either group, so non-parametric correlations
using Spearman’s rho were used.
Running Head: NSTUDY FOR ADHD 62

Students without ADHD
For the non-ADHD group, there were no significant correlations between self-reported
enjoyment and Highlighting or Other than Highlighting. However, these students tended to
enjoy themselves consistently: enjoyment before the prompt was significantly positively
related to enjoyment after the prompt (r = .876, p = .001). Also for students in the non-ADHD
group, independent fluency level was correlated significantly with enjoyment after the second
half of the passage (and after the self-monitoring prompt) (r = .658, p = .039), but not after the
first half (before the self-monitoring prompt).
Students with ADHD
In contrast, in the ADHD group, there were several significant negative correlations
between enjoyment and tactic use. Generally, the more students with ADHD enjoyed
themselves, the less likely they were to use tactics, or the more likely they were to use tactics,
the less likely they were to enjoy themselves. For this group, Enjoyment after First Half,
before the self-monitoring prompt were significantly negatively related to Total Tactic Use (r
= -.712, p = .021) as well as Tactic Use – Second Half (r = -.721, p = .019), where all
variables were normally distributed. The Highlighting variable, which was not normally
distributed for students with ADHD, was significantly negatively correlated to Enjoyment after
First Half using non-parametric correlations (rho = -.681, p = .03). Note that for the ADHD
group, the Total Tactic Use and Highlighting variables essentially represent the same behavior
because highlighting represented almost all the tactic use in the ADHD group.
Enjoyment in the second half trended toward significance in its association to tactic use
in the second half (r = -.617, p = .057).

Running Head: NSTUDY FOR ADHD 63

CHAPTER FIVE: DISCUSSION
Five research questions were posed at the beginning of the study. This section will
briefly discuss possible interpretations for the findings presented in the previous chapter.
Considerations, limitations and implications, and comments on future research follow.
Interpretation of Findings for Research Questions
Tactic Use in the Two Groups
The first two-part question, “1a. Does study tactic use differ between students with and
without ADHD when studying for a test in an online learning environment?” and 1b.“What
specific behaviors characterize the process of studying for students with and without ADHD?”
represent the heart of the current inquiry. Put simply, this study sought to discern self-
regulated learning profiles of middle school students with ADHD from those without ADHD.
Though the results should be interpreted with caution because of the small sample size and the
fact that the sample did not seem to include students of low socio-economic status, the
differences between the two groups for both highlighting and tactics other than highlighting
were significant at the p < .005 level.
Even a cursory glance at the total use for each tactic in the non-ADHD and ADHD
groups reveals dramatic differences in each group’s approach to the task (see Table 6 and
Figure 1 for a graph depicting the tactic use within the two groups). Despite a lack of literature
about the learning strategy use of students with ADHD, this passive approach to the studying
task is consistent with literature that has concluded that students with ADHD display fewer
behaviors associated with strategic academic engagement than their peers (R. Guare et al.,
2013; Kofman, Gidley Larson, & Mostofsky, 2008; Norwalk, Norvilitis, & MacLean, 2009;
Siklos, 2004; Vile Junod et al., 2006).
Running Head: NSTUDY FOR ADHD 64

Despite the differences between the two groups, both groups demonstrated a preference
for the most passive tactic available: highlighting. Highlighting comprised the majority of the
tactic use in both groups: it represented 69% of total tactic use overall, 68% of total tactic use
in the non-ADHD group and 80% of tactic use for the ADHD group. Dunlosky et al. (2013)
explain that although active highlighting (deciding what to highlight, as opposed to reading
material that has already been highlighted) should be an effective learning strategy, there are
several reasons that it is actually a passive, weaker strategy. First, the material that students
highlight is often not the most important material, and so even if they are engaging more
closely with the text than if they were only reading, they are engaging with the wrong pieces of
information. Second, students simply tend to highlight too much material. Though this study
did not formally count the number of words students highlighted, it should be noted that
several students highlighted more than one sentence in one highlight. Follow-up research
should both collect data on the amount of information being highlighted and the accuracy of
the participants’ identification of key points. Third, as they emphasize the importance of
highlighting effectively, the authors note that highlighting “may be problematic to the extent
that it prevents students from engaging in other, more productive strategies” (p. 21). This
tradeoff could certainly have applied to this study, as students in both groups leaned so heavily
on the highlighting tool.
ADHD and risk-taking. It could be the case that, while on average students in both
groups felt confident about that they would perform well on the quiz (which they were not
allowed to see ahead of time), the students with ADHD were willing to take the risk of not
employing tactics, whereas the students without ADHD wanted to ensure that they would be
prepared. The literature on risk-taking among students with ADHD is mixed: early research
Running Head: NSTUDY FOR ADHD 65

identified a greater inclination to take risks in everyday life (Barkeley, 1997; Barkeley, 1998,
as cited in (Toplak et al., 2005), but recent research on the risk-taking of performance of people
with ADHD has indicated mixed results. Much of this research has used gambling tasks with
adolescents and found that, while they do tend to choose less favorable outcomes, their risk
adjustment may not be significantly different from those without ADHD (Kroyzer, Gross-Tsur,
& Pollak, 2014; Toplak et al., 2005).
What these research designs seem to be missing is a differential in mental effort. The
instrument often used to simulate real-life decision-making in these studies is the Iowa
Gambling Task, which requires participants to bet money and choose cards. These tasks do not
include an expectancy value component, whereby a participant’s estimates his achievement
performance and use it, plus his value of the outcome, to make choices (Wigfield & Eccles,
2010). In the case of this study, the choice related to the effort that would be required to
minimize the risk of performing poorly on the quiz. In other words, in this study, students had
to do a cost-benefit analysis to determine whether it was worth the effort to employ tactics. In
one study (Fass & Schumacher, 1978), participants who were motivated financially to perform
well on a quiz benefitted more from highlighting than participants who were not offered the
money. Though most students in this study may have determined that, due to a combination of
their prior knowledge and comprehension of the passage, they would do fine on the test
(regardless of whether that turned out to be true), the students without ADHD chose to employ
tactics anyway, whereas the students with ADHD chose to take the chance on the quiz with
passive engagement with the text.
In fact, though both groups’ reading levels are comparable in terms of grade level, the
finer-grain measures of reading fluency – total miscues and meaning-changing miscues –
Running Head: NSTUDY FOR ADHD 66

reveal language-based weaknesses in the ADHD group. These weaknesses, taken in context of
the lack of tactic use, demonstrate poor metacognition or poor motivation within the ADHD
group that is consistent with research (Alvarado et al., 2011; Shiels & Hawk, 2010). In other
words, students with ADHD either did not notice that they needed to use more tactics to
interact more closely with the text or they did not want to. It is true that in the context of
performance on the quiz, these students may not have needed to employ more tactics, but they
took the risk. Ultimately students with ADHD may simply have calculated that the reward of
the quiz was not worth the mental effort required to employ tactics, much less elaborative ones.
The glossary hypothesis. The hypothesis that students with ADHD would be more
likely to click on the glossary was not supported by the findings. Because students with
ADHD have weaker executive functioning skills, including attention, and often have weaker
motivation than students without ADHD, it was hypothesized that they would use both passive,
low-utility tactics available: highlighting and clicking on the glossary
5
. Instead, only one
student with ADHD clicked on the glossary once, whereas eight out of 10 students without
ADHD clicked on the glossary, each between one and four times.
The passage may have been too easy, which could explain a lack of tactic use overall,
but particularly in the case of glossary use as students may have known the meanings of the
words. Indeed, Fass and Schumacher (1978) found that subjects who were given a more
difficult passage to read benefitted more from highlighting (in the case of their study,
underlining) than those who read an easier passage. But the reading level of the students
without ADHD was as high or higher than those with ADHD, so they would be more likely to

5

K.
Rawson (personal communication, August 6, 2014) noted that some students may
spontaneously engage in elaborative processing, but since this study did not include an
extensive think-aloud process, it is not known which students’ cognition include elaboration or
connection with prior knowledge.

Running Head: NSTUDY FOR ADHD 67

have at least a working knowledge of the terms included in the glossary, and therefore less
likely to need it. So it stands to reason that the ADHD group should have been more likely to
click on the glossary.
This difference may also be explained by the expectancy value theory (Wigfield &
Eccles, 2000), in that students with ADHD may have determined that clicking on the glossary
would not have been likely to aid their comprehension or retention of the information in the
passage, and was therefore not worth the effort. This explanation is consistent with literature
that demonstrates resistance to implementation of learning strategies among weaker students
(Bell & Limber, 2010; Dembo & Seli, 2004). Furthermore, reduced activity in prefrontal brain
regions among students with ADHD may represent increased effort associated with the
application of learning strategies, which would have factored into their assessment of value.
Self-Monitoring Prompts
The research question, “Do self-monitoring prompts during studying affect the use of
study tactics differently between the two student groups?” represents an inquiry into a field of
research that is also lacking. Rock (2005) conducted and evaluated self-monitoring training for
elementary school students with and without exceptionalities (n = 9) and found promising
results in response to training. But no large-scale or quantitative analyses of the effects of self-
monitoring prompts on students with ADHD have been found. The purpose of this inquiry was
to determine whether students with and without ADHD in an online learning environment
would respond to self-monitoring prompts consistent with the study conducted by Kauffman,
Zhao and Ying (2011), who found that the prompts increased both the quantity of notes taken
and the score on a post-test. That study used college-aged participants without known
Running Head: NSTUDY FOR ADHD 68

disabilities, and in general, literature on the use of self-monitoring and the effectiveness of
prompts has been so mixed that the hypotheses around self-monitoring prompts were tentative.
The failure of paired-sample t-tests to reach statistical significance in the non-ADHD
group may be related to the small sample size. It may also be related to a lack of change in
response to the self-monitoring prompt among the three students with ADHD who chose to use
no tactics in either the first or second half. Both a tally of responses to the prompt and the
mean differential in tactic use between the first and second halves do seem to show differences
(although these differences were not statistically significant) between the two groups, with
students without ADHD responding to the prompt with an increase in tactic use and students
with ADHD responding with a decrease in tactic use.
With such a small range of tactic use within the ADHD group (0 to 4), it is also difficult
to interpret responses to a self-monitoring prompt. A longer passage might constituted
increased cognitive load to process information in the text; that demand coupled with
additional time expended during the first half relative to this study’s design may increase
responses to self-monitoring prompts.
Learning Outcome
Results for the research question, “Is tactic use associated with learning outcomes, as
measured by the criterion-referenced quiz?” were surprising, as quiz scores between the two
groups did not differ much and tactic use was unrelated to performance on the quiz. Because
this study's focus is on self-regulated learning, the purpose of the reading comprehension quiz
was to compare students with ADHD and without in both their use of strategies and the
effectiveness of those strategies, as measured by the reading comprehension task. Although the
task is measuring a learning outcome, its primary purpose was to compare the two groups.
Running Head: NSTUDY FOR ADHD 69

There are several possible reasons for the absence of a significant correlation between
tactic use and performance on the quiz in both groups. The most obvious possibility is the
small and socioeconomically homogenous sample size. Though the average quiz score in the
non-ADHD group (M = 7.50) was only slightly higher than that in the ADHD group (M =
7.00), with a larger, more diverse sample the difference may have been stronger and a
correlation between tactic use and quiz score may have emerged. Furthermore, the small range
of tactic use among the ADHD group (0 to 4) presented challenges to statistical analysis.
Quantitative and qualitative variation in individual student’s use of learning tactics may
also account for the lack of pattern of effectiveness of tactics. This study did not evaluate the
effectiveness of the tactics used; perhaps the incorporation of qualitative analysis would reveal
a correlation between certain qualities in tactic use (e.g. highlighting fewer words at a time, or
making notes that include specific terms) and performance on the quiz. The dependence on
highlighting may have played a role. Though the correlation remained non-significant for the
Other than Highlighting category, as Dunlosky et al. (2013) note, the opportunity cost of
highlighting was the mental effort that might have been used for other tactics (p. 21). Though
this study was interested primarily in creating the most authentic study-for-a-test task possible,
additional research can remove highlighting from the menu of available tactics and explore the
correlation with learning outcome. Perhaps, consistent with the findings in the Dutch study, it
was the time spent using the tools that correlated with performance, as opposed to the number
of instances of tactic use tallied in this study (Juarez Collazo et al., 2014).
Prior knowledge plays an important role in moderating the relationship between
instruction or exposure to material and learning outcomes (Cromley & Azevedo, 2007b;
Leopold & Leutner, 2012; Moos & Azevedo, 2008; Vrugt & Oort, 2008). Since the correlation
Running Head: NSTUDY FOR ADHD 70

between prior knowledge in the non-ADHD group was significant, students in that group seem
to have been able to access that prior knowledge. Their performance on the quiz would then
have been some function of information they already knew and their ability to incorporate the
new information from the passage into their existing schema. However, the lack of
relationship between prior knowledge and quiz performance in the ADHD group suggests that
those students may not have leveraged their existing knowledge to help them succeed at the
task at hand with the consistency that the students without ADHD did.
The role of reading ability in the quiz performance among students with ADHD is also
concerning. While prior knowledge and reading ability have long been known to correlate
with performance, the fact that students without ADHD used prior knowledge and students
with ADHD were less able to do so is noteworthy. The role of reading fluency in quiz
performance among students with ADHD also highlights the challenges associated with
participants with ADHD, as so many of them suffer from comorbid language-based learning
difficulties that affect their reading ability.
That said, students with ADHD may not have realized their comprehension was weaker
than it needed to be in order to perform well on the quiz. Perhaps if students with ADHD read
the passage and determined it was too easy for them and so made the impulsive decision not to
employ tactics. If their reading and basic reasoning skills are intact, and their working memory
is not too weak, they may do well on the quiz without employing tactics. Conversely, students
at the high end of the range of tactic use, who would have used 3 or 4 tactics, may have
become over-confident with a passage below their reading level and therefore not prepared
sufficiently for the quiz.

Running Head: NSTUDY FOR ADHD 71

Enjoyment
Because available literature was sparse on the specific enjoyment construct related to
the last question, “Is self-reported enjoyment before and after the prompt associated with
tactic use for one or both of the groups?” it was not entirely clear how students’ enjoyment
and tactic use might be related.
In the non-ADHD group, the consistency of students’ enjoyment between the first and
second halves of the passage and the lack of correlation between tactic use and enjoyment
could speak to the stability of affective reactions to learning among typical students. Though
measurements of anxiety and stress fall outside the scope of this study, the significant negative
correlations between enjoyment and tactic use in the ADHD group could mean that students
with ADHD experienced more anxiety and stress than students without ADHD. Research has
shown that students with ADHD – particularly those with more symptoms of inattention –
frequently suffer from comorbid anxiety (Gaub & Carlson, 1997; Loe & Feldman, 2007) and
stress has been found to moderate academic enjoyment in students (Carlson et al., 2002).
In contrast to students without ADHD, for whom achievement and engagement are
typically correlated (Ely et al., 2013) and for whom challenge and enjoyment are typically
correlated (Abuhamdeh & Csikszentmihalyi, 2012), students with ADHD may have reported
lower enjoyment with increased tactic use because they found the additional work unpleasant.
This interpretation would be consistent with literature that has found that students with ADHD
prefer easier work (Carlson et al., 2002). Because students with ADHD often process
information more slowly than typical students (due to reductions in white matter in the
prefrontal regions), the labor involved in what would otherwise be considered an appropriate
level of challenge may render the task less enjoyable to them.
Running Head: NSTUDY FOR ADHD 72

Neurologically speaking, the complexity of SRL tasks necessitates the involvement of
several brain regions, including those that process dopamine, the neurotransmitter released
when people experience a sense of reward. Though research has identified abnormalities in the
dopaminagic pathways among people with ADHD (Schneider, Retz, Coogan, Thome & Rosler,
2006; Sharp, McQuillin & Gurling, 2009; Sonuga-Barke, 2002), a clear profile of reward
processing and the role of dopamine with ADHD has not yet been identified.
Alternatively, because enjoyment in the first half of the passage was significantly
negatively related to tactic use in the second half, students with ADHD may have reported
increased enjoyment because they entered a state of “flow” (Csikszentmihalyi, 1991), which
has been found to preclude focused SRL (Reed, Schallert, & Deithloff, 2002).
Measurement of Self-Regulated Learning as an Event
One of the primary aims of this study was to contribute to the literature on the
measurement of SRL a more direct measure of actual behavior than most extant measures
provide. The procedures used in this study allowed for data collection about the actual choices
students make in a study-for-a-test context. This representation of “SRL as an event”
represents a more authentic characterization of a student’s approach to this kind of learning
task than a checklist or Likert scale asking students to self-report the extent to which they
engage in SRL tactics and strategies. Cromley and Azevedo (2007) found that the results of
such self-reports were not accurate measures of student behavior, but that the results of think-
aloud protocols were. Though they did not collect data on the presence of ADHD or learning
disabilities, their participants were not in pull-out special education classes and did not attend
schools for students with learning problems. It stands to reason that students who are impaired
in areas of the brain that regulate and monitor cognition and behavior may be even less reliable
Running Head: NSTUDY FOR ADHD 73

self-reporters. The focus of this research was not to prove the faultiness of those assessments,
but to work from the assumption of their faultiness and to put forth a method that can be used
by clinicians to gather valuable quantitative and qualitative data about a student’s approach to a
studying task.
Considerations and Limitations
In addition to the small sample size, there are several considerations that should be
taken in the interpretation of findings and limitations to its generalizability.
Sample Population
Not only the size, but the demographic homogeneity and variation among individual
learner characteristics should be considered. First, due to self-selection, the sample was more
likely to include proactive families who are involved in the assessment and intervention in their
children’s learning and the treatment of their children’s ADHD. Second, the sample seems
only to have included families of high socioeconomic status. In the case of the school site, this
phenomenon is likely related to the high cost of the independent school that participated. In
the case of the rest of the students, the phenomenon is likely due to the location of data
collection sites, the social networks of people who participated in word of mouth recruitment,
and the time and flexibility required to visit the office for data collection. Inclusion of a more
diverse group of children might result in learning outcome findings more consistent with
expectations (and most of the literature) and allow for greater generalization.
Furthermore, comorbid learning, behavioral and mood problems common among
students with ADHD always present potential confounds in data analysis. Several of the
students in the non-ADHD group also had been diagnosed with learning disabilities, which
Running Head: NSTUDY FOR ADHD 74

may not affect their approach to the task (as measured by tactic use), but could affect their
learning outcomes and enjoyment.
Two Students with ADHD, Hyperactive Type
The presence of ADHD was determined by two factors: parent-reported past diagnoses
from a clinician and completion of items from the VADPRS corresponding to symptoms of
inattention and hyperactivity-impulsivity. Parent-reported diagnosis of ADHD has been found
to be valid in southern California (Visser, Danielson, Bitsko, Perou and Blumberg, 2013).
In developing the research design, the question of whether or not to omit students with
a diagnosis of ADHD, Hyperactive Type was considered. As discussed in Section 2, these
students typically do not display the same patterns of executive functioning weaknesses as
students diagnosed with the other two subtypes, Inattentive Type and Combined Type, both of
which require the presence of a detrimental level of inattention (American Psychiatric
Association, 2000). In contrast, ADHD, Hyperactive-Impulsive Type has been found to be
associated more strongly with delinquent and aggressive behaviors than academic problems
(Willcutt, 2014).
Parents reported Hyperactive Type diagnoses for two students who were recruited into
the study. Due to widespread concerns about misdiagnosis (Elder, 2010; Hartnett et al., 2004),
coupled with an increase in academic demands that can make learning difficult even for
students with a hyperactive profile (and therefore the possibility that students who were
diagnosed with ADHD, Hyperactive Type may also have symptoms of inattention that affect
learning, the students were included in the study. Furthermore, information about medication
for ADHD fell outside the scope of this study; if children with ADHD are on medication to
alleviate symptoms, parents may be endorsing items that correspond to the medicated behavior.
Running Head: NSTUDY FOR ADHD 75

Upon conducting data analysis, the two students with ADHD, Hyperactive Type were
two of the only three students to use more than one tactic. Because the mean total tactic use in
the ADHD group was 1.5 tactics, if those two students had been included in the non-ADHD
group, differences between the two groups in tactic use would have been even starker.
Passage Difficulty
Passage selection for the study task was challenging for this group of participants.
Ultimately the seventh grade level passage was chosen partly because the participating school
site is a school for children with learning disabilities, many of whom have language-based
learning problems. However, the ease of the passage may have affected the glossary use
behavior in both groups of students. As Winne and Jamieson-Noel (2002) point out, it is
critical in clinical observation of SRL that the learning context either be “mildly atypical” or
use content “challenging enough that it invites students to seek more effective study tactics” (p.
570).
Hawthorne Effect
Finally, though a direct measure of SRL like the one that nStudy allows has potentially
more face validity than self-reports, the disadvantage of measuring SRL in the context of a
research study is that students may behave in ways they believe are associated with socially
desirable outcomes, rather than behavior that truly represents their quotidian habits. This
Hawthorne effect may have applied to some of the students in the non-ADHD group who
enacted the most tactics.
Implications
This study addresses a gap in the literature available on the specific learning strategy
use or study habits of school-aged children, since most of the extant literature does some
Running Head: NSTUDY FOR ADHD 76

combination of the following: focuses on college-aged students, relies of self-reports of SRL,
and measures strategy use with a singular, clinical task that may not have much ecological
validity to a study-for-a-test scenario. Specifically, this study contributes ecological validity to
the body of research about study habits. In contrast to literature that uses self-reported strategy
use as a proxy for SRL, this study simulated an authentic study-for-a-test context. Parents and
educators whose questions about studying are often stymied by middle school students’
insistence that they “studied” can use the design and results from this study to ask more
pointed questions about a child’s independent learning process as he prepares for a test.
One of the disheartening implications of this study relates to the prognosis for response
to training in learning strategies for a student with ADHD who suffers from weak executive
functioning and low motivation. Participants in this study were told that the purpose of the
study was to understand better the study habits of middle school students. They were trained in
five learning strategies and provided with a quick reference guide to aid their use. They were
told to “study as you would for a test you really cared about,” a researcher sat behind them and
took notes on their behavior, and halfway through the passage, they were prompted with a
reminder to use the tactics. Despite the training, support and prompting, students with ADHD
– particularly with inattentive-type or combined-type ADHD – barely employed any tactics. If
students with ADHD decline to engage in tactics in a context like the one in this study, how
likely are they to implement the strategies at school and at home that they learn in one to two
hours per week with an educational therapist?
One answer is that learning strategies training for students with ADHD must be
incorporated into school curricula, particularly in regular classroom instruction. Furthermore,
Running Head: NSTUDY FOR ADHD 77

parents of children with ADHD should recognize that such training can be useful, but is likely
insufficient toward goals of implementation of learning tactics.
Application of Winne and Hadwin’s Four-Phase Model
With regard to Winne and Hadwin’s (1998) model of SRL, this study included all four
of the phases: task definition, goal setting, enactment and adaptation, but focused on the second
two phases of the four-phase model. The first two stages, task definition and goal setting were
accomplished by the study design. Of course, students had the opportunity to regulate their
cognitions about the task by asking clarifying questions, which many of them did. The goal
was stated for participants: to perform as well as they could on the quiz, but the development
of a plan of tactics to enact occurs in the goal-setting stage. In this study, that stage likely
corresponded to the training phase during which participants were learning about the five
tactics that were available to them. It is in the training phase that a learner should generate
meaning about the task and the tactics available and begin to formulate a plan for enactment.
The heart of this study lay in the model’s third stage, enactment, during which students applied
the plan the developed for the use of tactics, or as may be the case with students who did not
use tactics, the stage during which passivity in learning was able to be measured. The fourth
stage, adaptation, relies on metacognitive awareness, as learners incorporate feedback from
either their own cognition or the task (in the form of traces of tactics saved in nStudy’s pane).
The self-monitoring prompt encouraged adaptation and thereby offered learners a moment of
true self-regulation wherein they could consider their own tactic use and adapt in real time to
adjust for the second half of the passage.
Though the passage was perhaps too short for students to have forgotten the available
tactics, the failure of the students in the ADHD group to respond is striking. None of the
Running Head: NSTUDY FOR ADHD 78

participants in the study displayed oppositional behavior or indicated verbally distaste for the
task or a refusal to enact tactics. In fact, most of them were quite cheerful and acknowledged
understanding after the training. Yet their tactic use was minimal and they essentially ignored
the opportunity to adapt their learning.
Directions for Future Research
nStudy as a Data Collection Tool to Profile a Student’s SRL
Though an explicit question about the effectiveness of nStudy as a descriptive tool fell
outside the scope of this study, both quantitative and qualitative data allow for a preliminary
assessment of its utility in this context. In terms of task design, nStudy allowed for the
customization of a task to mimic closely the type of studying a middle school student would
have to do before a test. Though it did not require synthesis among multiple sources, students
are increasingly required to read and study electronic material in preparation for in-class
assessments.
Students with and without ADHD demonstrate starkly contrasting patterns of tactic use
in nStudy suggests that, with further research, it may be validated as part of a multifaceted
diagnostic process for students who have either demonstrated difficulty in school or have
already been diagnosed with ADHD. A simple task using nStudy in tandem with any online
test would allow a psychologist or educational therapist to determine a baseline of study habits,
as well as to monitor progress after prolonged training in learning strategies.
Prior Knowledge
Several significant findings from this study are worthy of further research, particularly
because of a lack of published literature about these topics. Prior knowledge was associated
with quiz score for students in the non-ADHD group, but not for students in the ADHD group.
Running Head: NSTUDY FOR ADHD 79

Follow-up research could explore the relationship between prior knowledge and quiz scores for
students with ADHD compared with those without. Though educational researchers tend to
agree on the strong role of prior knowledge, difficulties in the ability to leverage prior
knowledge resources in a learning task among students with weak executive functions should
be explored.
Enjoyment
The significant relationships between enjoyment and tactic use should also be
researched further. A challenge in research on enjoyment is the operationalization of the term.
Further research can apply the characterization of it as “involvement” and explore the
hypothesis about such a state precluding SRL (Reed et al., 2002); testing that hypothesis
among students with ADHD could also make a much-needed contribution to the body of
literature on affective characterizations of students with ADHD in learning contexts.
A More Difficult Passage
Participants in the study were in their second semester of their seventh- or eighth-grade
year, yet the passage was leveled at grade 7.5. It could be that reading a more difficult passage
would prompt students to employ more tactics in order to seek understanding. As long the
passage were not so difficult as to cause students with ADHD to drop out of the task entirely, it
could provide a more nuanced picture of the differences between the two groups while
studying for a passage for which they needed to employ tactics. It is likely that the non-ADHD
group would be more adept at recognizing their weakness and calibrating their tactic use
accordingly (Fernandez-Duque, Baird, & Posner, 2000; Juarez Collazo et al., 2014).
Finally, the self-monitoring prompts in this study were generic, meaning that all
learners received the same prompt at the same point during the task regardless of their behavior
Running Head: NSTUDY FOR ADHD 80

hitherto. Azevedo et. al (2005) suggest that an “adaptive scaffolding” condition, in which a
human tutor assists their activation of prior knowledge and facilitates their self-monitoring and
strategy use.
Neurological correlates of ADHD
As discussed in Chapter Two, neurological differences have been found between
people with and without ADHD in the areas of the brain that deal with the higher-order
cognitive processes with which people with ADHD struggle including: organization, planning,
monitoring, executive control, and working memory. The micro decisions involved in
participation in this study, including deciding if and when to apply which tactic to which piece
of information, do appear to correspond to the types of complex decision-making frequently
associated with the prefrontal cortex, which has been found to be hypoactive Schneider, Retz,,
Coogan, Thome & Rösler, 2006). Further study could incorporate fMRI measures to explore
differences in activation of the prefrontal and other brain regions of students with and without
ADHD as they complete a task similar to the one in this study.
Summary
Winne and Hadwin’s (1998) four-phase model of SRL provides a useful heuristic to
consider the SRL profiles of students with ADHD. Despite context that supported goals,
minimization of cognitive load, and prompting, students with ADHD chose to enact very few
tactics during their online study-for-a-test task. Furthermore, students with ADHD reported
reduced enjoyment associated with their use of tactics. Though educators and parents often
expect students with ADHD to try harder or to benefit passively from learning strategies
training or educational therapy, the struggles associated with ADHD are profound. But very
often students with ADHD are willing and ready to put forth a great deal of mental effort
Running Head: NSTUDY FOR ADHD 81

toward their learning, but do not know how to implement effective strategies, and furthermore
often do not even know that their own methods are passive or ineffective. The procedures used
in this study allow a clinician to unpack the layers of qualitative information that underlie a
student’s poor performance on a quiz or ineffective studying. With that process, a more
nuanced understanding can evolve within the student with ADHD about his or her disorder and
its effects on learning, and with that valuable information he or she may approach training in
learning strategies with a more open mind.

Running Head: NSTUDY FOR ADHD 82

TABLES

Table 1

Summary of Reported Demographic Information

Statistic School Type Gender Age Grade Ethnicity
Learning
Disability
Strategic
Training
Non-ADHD
n= 10
Public=3
Independent=7
Male=4
Female=6
12=1
13=5
14=4
7th=4
8th=6
White=7
Mixed=2
Asian=1
Yes=2
No=8
Yes=4
No=6

ADHD
n= 10
Public=2
Independent=8
Male=9
Female=1
12=1
13=2
14=7
7th=4
8th=6
White=9
Mixed=1
Yes=7
No=1
Not sure=2
Yes=7
No=2
Not sure=1

Running Head: NSTUDY FOR ADHD 83

Table 2
Parametric Correlation Matrix for Students without ADHD
Total tactic use
Other than
Highligting
Highlights
Tactic Use -
First half
Tactic Use -
Second Half
Independent
Fluency GL
Instructional
Fluency GL
Oral Reading
Miscues
Meaning-
changing
Miscues
Enjoyment
after First Half
Enjoyment
after Second
Half
Prior
Knowledge
Score
.545
.871
**
.062
.869
**
.53
.724
*
.952
**
.483
.850
**
.677
*
-.047 .078 -.101
.088 .417 -.325 .715 -.326
1.000
**
-.168 -.134 -.121 -.041 .003 -.139
-.928
*
-.028 -.089 .019 -.326 .159 -.345
-.907
*
.951
**
-.151 -.171 -.079 -.423 .037 .385 -.59 .201 .139
-.068 -.043 -.055 -.348 .114
.658
*
-.802 .322 .2
.876
**
.243 .414 .047 .18 .251
.693
*
.423 -.057 -.176 .013 .435
.297 .522 .048 .419 .184
.715
*
.729 -.38 -.481 -.143 .204
.884
**
* Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the 0.01 level (2-tailed)
Prior
Knowledge
Score
Quiz Score
Instructional
Fluency GL
Oral Reading
Miscues
Meaning-
changing
Miscues
Enjoyment
after First Half
Enjoyment
after Second
Half
Tactic Use -
Second Half
Independent
Fluency GL
Tactic Use -
First Half
Other than
Highligting
Highlights
Running Head: NSTUDY FOR ADHD 84

Table 3
Parametric Correlation Matrix for Students with ADHD
Total tactic use
Other than
Highligting
Highlights
Tactic Use -
First half
Tactic Use -
Second Half
Independent
Fluency GL
Instructional
Fluency GL
Oral Reading
Miscues
Meaning-
changing
Miscues
Enjoyment
after First Half
Enjoyment
after Second
Half
Prior
Knowledge
Score
-.218
.963
**
-.473
.926
**
-0.355
.934
**
.804
**
.066
.708
*
.52
.1 -.614 .243 0 .229
.194 -.73 .394 .201 .091
1.000
**
.073 .635 -.106 -.184 .475 -.046 -.222
.122 .642 -.064 -.082 .422 -.173
-.905
**
.680
*
-.712
*
.382
-.748
*
-0.565
-.721
*
-.166 -.498 -.003 -.017
-.455 .05 -.424 -.261 -.617 -.131 -.247 -.169 -.293 .447
-.382 .294 -.426 -.323 -.356 -.17 -.214 .422 .19 .15 .549
.096 -.314 .173 .207 -.108 .26 .699 -.042
-.687
*
-.107 .409 .436
* Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the 0.01 level (2-tailed)
Prior
Knowledge
Score
Quiz Score
Instructional
Fluency GL
Oral Reading
Miscues
Meaning-
changing
Miscues
Enjoyment
after First Half
Enjoyment
after Second
Half
Independent
Fluency GL
Other than
Highligting
Highlights
Tactic Use -
First Half
Tactic Use -
Second Half
Running Head: NSTUDY FOR ADHD 85

Table 4
Non-parametric Correlation Matrix for Students without ADHD
Total Tactic
Use
Highlighting
Other Than
Highlighting
Tactic Use -
First Half
Tactic Use -
Second Half
Enjoyment
after First Half
Enjoyment
after Second
Half
Independent
Fluency GL
Instructional
Fluency GL
Oral Reading
Miscues
Meaning-
changing
Miscues
Prior
Knowledge
Score
Quiz Score
.777
**
* Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the 0.01 level (2-tailed)
.309
-.065
-.048
.544
Prior
Knowledge
Score
-.04 .413
.548 Quiz Score .055
Oral Reading
Miscues
.531
.837
**
.411
.875
**
.497
-.401 -.016
-.348 .079
-.289 .211
.363
.133
-.222
-.354
-.218 -.725 .959
**
.281 .205 -.106 .174 .546
Instructional
Fluency GL
.695
*
.062
.720
*
-.211
-.075 .222 .223 .538 .670
*
.354 .05 -.054 .790
**
.725 -.337 -.382
.296 .141 .221
-.559 -.745 1.000
**
-.346 .098 .252 .374 -.027 -.707
.725
-.505
.505
-.022 .889
**
-.199
-.141
0
-.245
-.072
-.131
.053
Meaning-
changing
Miscues
-.505 -.068
-.207 -.205 .49 .739
*
Independent
Fluency GL
Highlighting
Other Than
Highlighting
Tactic Use -
First Half
Enjoyment
after First Half
Tactic Use -
Second Half
Enjoyment
after Second
Half
Running Head: NSTUDY FOR ADHD 86

Table 5
Non-parametric Correlation Matrix for Students with ADHD
Total Tactic
Use
Highlighting
Other Than
Highlighting
Tactic Use -
First Half
Tactic Use -
Second Half
Enjoyment
after First Half
Enjoyment
after Second
Half
Independent
Fluency GL
Instructional
Fluency GL
Oral Reading
Miscues
Meaning-
changing
Miscues
Prior
Knowledge
Score
Quiz Score
* Correlation is significant at the 0.05 level (2-tailed)
** Correlation is significant at the 0.01 level (2-tailed)
Quiz Score
Highlighting
Other Than
Highlighting
Tactic Use - First
Half
Enjoyment after
First Half
Tactic Use -
Second Half
Enjoyment after
Second Half
.176
.142 -.348 .272 -.055 -.012 .41 .249 .718 -.18
-.717
*
.396
-.214 .275 -.192 -.354 .247 .519 -.166 -.087 .536
-.052 -.124 -.479
.236 .598 -.083 .382 -.03 -.419 -.207
-.798
*
.772
*
0 -.73 .087 .091 -.399 -.083
1.000
**
-.418 .039 -.177 -.603 .516
.805
**
.126 .464
-.287
.321
Prior Knowledge
Score
Oral Reading
Miscues
0
.871
**
-.326
.595
-.681
-.381
.328
.394
-.135
-.075
.383 -.532
-.696
*
-.579 .01 .125 -.047 -.136
.852
**
.913
**
-.516
Instructional
Fluency GL
Meaning-
changing
Miscues
-.609
.039
.209 .642 -.124 .394 .055
Independent
Fluency GL
Running Head: NSTUDY FOR ADHD 87

Table 6

Tactic Use: Frequency and Number of Students

Group
Non-ADHD
(n = 10)
ADHD
(n = 10)
Total Number
of Students
Total Tactic
Frequency
Number of
Students
Total non-ADHD
Group Frequency
Number of
Students
Total ADHD
Group Frequency

Highlighting 9 90 4 12 13 102
Other than Highlighting 10 43 3 3 13 46
Notes 6 15 0 0 6 15
Tags 3 10 1 1 4 11
Visits to Glossary 8 16 1 1 9 17
Visits to Websites 2 2 1 1 3 3
Total Tactic Use 10 133 7 15 17 148

Running Head: NSTUDY FOR ADHD 88

Table 7

Summary of Responses to Follow-up Questions about Tactic Use

Which strategy was
most useful to you?
Is this how you
normally study?
If this software were
available to you,
would you use it?
Non-ADHD
n= 8
Highlighting=6
Notes=1
Glossary=1
Yes=5
It depends=1
No=2
Yes=5
It depends=2
No=1
ADHD
n= 7
Highlighting=2
Notes=2*
Tagging=1
Other=1
None=1
Yes=2
It depends=3
No=2
Yes=4
It depends=3

*Neither of these students created any notes.

Running Head: NSTUDY FOR ADHD 89

Table 8
Tactic Use and Enjoyment Before and After Self-Monitoring Prompt

ADHD
(n = 10)
Non-ADHD
(n = 10)
Group
M (SD) M (SD)
Tactic Use
before prompt
0.9 (1.13) 5.5 (3.1)
Enjoyment
before prompt
5.5 (3.3) 7.3 (1.57)
Tactic Use
after prompt
0.6 (.71) 7.8 (5.03)
Enjoyment
after prompt
6.8 (2.88) 7.1 (1.06)

Running Head: NSTUDY FOR ADHD 90

FIGURES

Figure 1: Tactic use and number of students in non-ADHD and ADHD groups.

Running Head: NSTUDY FOR ADHD 91

Figure 2: Response to self-monitoring prompt. Number of students in each group who
increased, did not change and decreased their tactic use in response to the self-monitoring
prompt.

0

1

2

3

4

5

6

7

8

Increased No Change Decreased
Number of
Students
Non-‐ADHD

ADHD

Running Head: NSTUDY FOR ADHD 92

Figure 3: Prior knowledge and quiz scores for students without ADHD.

Running Head: NSTUDY FOR ADHD 93

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APPENDICES
Appendix A: USC IRB Approval

9/12/14, 12:26 PM
Page 1 of 3 https://istar.usc.edu/iStar/Doc/0/96U36N65JDHKNF7EUI66BCTS1F/fromString.html

UNIVERSITY OF SOUTHERN CALIFORNIA
UNIVERSITY PARK INSTITUTIONAL REVIEW BOARD
FWA 00007099
Approval Notice for Expedited Review Applications

Date: Dec 23, 2013, 10:18am
Principal Investigator: Nicole Saloun, MPP
ROSSIER SCHOOL OF EDUCATION
Faculty Advisor: Gale Sinatra
ROSSIER SCHOOL OF EDUCATION
Co-Investigators:
Robert Danielson
ROSSIER SCHOOL OF EDUCATION
Project Title: Eighth-Grade Students in nStudy
USC UPIRB # UP-13-00447

The University Park Institutional Review Board (IRB) designee determined that your project meets the requirements
outlined in 45 CFR 46.110 category (7) to receive expedited review. The IRB designee determined that this research
involves no more than minimal risk. In approving this research it was determined that all of the requirements under
45CFR 46.111 were satisfied. Minors are eligible to participate under 45CFR46.404. The study was reviewed and
approved on 12/22/2013.

The study has been approved for a period of one year. If you plan to continue this study next year, you are required
to submit a continuing review application prior to its expiration date of 12/21/2014. You may not enter subjects on
the study before IRB approval or if IRB approval expires.

The following documents were reviewed and approved:
Approved Child Assent Form, dated 12-09-2013
Approved Youth and Parental Permission Form, dated 12-09-2013
Approved Information Sheet for Parents, dated 12-09-2013
Approved Recruitment Script, dated 12-09-2013
Minor revisions were made to the recruitment and consent documents by the IRB Administrator (IRBA). The IRBA
Running Head: NSTUDY FOR ADHD 109

9/12/14, 12:26 PM
Page 2 of 3 https://istar.usc.edu/iStar/Doc/0/96U36N65JDHKNF7EUI66BCTS1F/fromString.html
revised documents have been uploaded into the relevant iStar sections. Please use the IRBA revised documents as a
template, if an amendment is submitted and future revisions are required.
The school information has been removed from the recruitment and consent documents. If school sites are
added, please submit an amendment. At the time of an amendment, the IRBA revised documents uploaded
into section 24 can be revised to include the school language; however, it is not necessary to list the school sites
on the recruitment, consent and assent documents.

As the Principal Investigator you are required to ensure that this research and the actions of all project personnel
involved in conducting the study will conform with the research project and its modifications approved by the IRB;
HHS regulations (45CFR46); IRB Policies and Procedures and applicable state laws. Failure to comply may result
in suspension or termination of my research project, notification of appropriate governmental agencies by the IRB,
and/or suspension of your freedom to present or publish results. Any proposed changes in the research project must
be submitted, reviewed and approved by the IRB before the change can be implemented. The only exception is a
change necessary to eliminate apparent immediate hazards to the research subjects. In such a case, the IRB should
be informed within 5 days of the change following its implementation for IRB review. You must inform the IRB
immediately if you become aware of any violations of HHS regulations (45CFR46), applicable state laws or IRB
Policies and Procedures for the protection of human subjects. You are required to notify the IRB office in the event
of any action by the sponsor, funding agency, including warnings, suspension or termination of your participation in
this research. You must maintain all required research records and recognize the IRB is authorized to inspect these
records. A final progress report is required by the IRB upon completion or termination of the study.

Informed consent is obtained in the research participant’s language. If the participant speaks Spanish and the
informed consent document has been translated into Spanish, you must utilize the Spanish informed consent
document, the Spanish Experimental Subject's Bill of Rights and the Spanish HIPAA Authorization form. For
participants who speak other languages, you must have a translator verbally translate the English informed consent
document into those languages for the participants. The English informed consent serves as a summary. The
translator, the person obtaining informed consent and the witness sign the English informed consent document. The
participant and witness sign the Short Form informed consent document, which must be in the participant’s
language. The IRB has translated the Short Form consent into multiple languages, which are available on the IRB
website. In addition, the participant signs the Experimental Subject's Bill of Rights in the participant’s language.
The IRB has translated the Experimental Subject's Bill of Rights into multiple languages which are also available on
the IRB website (http://www.usc.edu/admin/provost/oprs/hsirb/forms).

You must inform the IRB of any unanticipated adverse event or injury no later than 14 calendar days following the
time it becomes known that a subject suffered an adverse event/injury. To report adverse events you must use the
Reportable Event activity in iStar. Furthermore you must inform the IRB immediately of any significant negative
change in the risk/benefit relationship of the research as originally presented in the protocol and approved by the
IRB.

Sincerely,

Richard S. John, Ph.D., Chair

Approved Documents: view

Funding Source(s): N/A - no funding source listed
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Appendix B: Student Word Lists

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Appendix C: Examiner Word List
sts
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Appendix D: Oral Reading Fluency Passage

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Appendix E: Prior Knowledge Assessment

8/10/2014 Stars Prior Knowledge
https://docs.google.com/forms/d/16SmQix9e2qTb2pOqS3nDuMNbxaaYaKCtdoigAVhnguE/viewform 1/2
Stars Prior Knowledge
* Required
What is your 4-digit ID Number?
1. What are stars made of? *
2. What is gravity? *
3. What is mass? *
4. What do nuclear reactions produce? *
S u b m i t
Never submit passwords through Google Forms.
E d i t t h i s f o r m
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Appendix F: Stars Passage

8/10/2014 Stars 1
http://perspectivesed.com/nstudy-v1/nstudy-6.php 1/2
Star Formation - Part 1
Stars have life cycles, just like humans. In fact, a star is born,
changes, and then dies. In contrast to the human life cycle that
lasts about 75 years, the life cycle of a typical star is measured
in billions of years.
Every star in the sky is at a different stage in its life cycle. Some
stars are relatively young, while others are near the end of their
existence. The sun is about halfway through its 10-billion-year-
long life cycle.
Birth of a Star
The space between stars is not entirely empty. In some places,
there are great clouds of gas and dust. Each of these clouds is
a nebula. A nebula is where stars are born.
Spitzer's Orion. Image Credit: NASA, JPL-Caltech, T. Megeath (Univ. Toledo, Ohio)
The element hydrogen makes up most of a nebula. Helium and
a sprinkling of dust are also present. The particles in a nebula
are spread very thin. In fact, the particles are a million times less
dense than the particles in the air you breathe. However, since
nebulae are very large, they contain enormous amounts of
matter.
< previous | next >
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8/10/2014 Stars 3
http://perspectivesed.com/nstudy-v1/nstudy-8.php 1/1
Star Formation - Part 2
Gravity causes matter to be attracted to the other matter.
Therefore, as a nebula travels through space, it collects more
dust and gas. The clouds become packed tighter and tighter, as
gravity pulls it all together. Whenever matter is packed in this
way, it heats up. An especially dense part of the nebula may
form a hot, spinning ball of matter. Such a ball of hot matter is
called a protostar.
A protostar doesn't yet shine by ordinary light, but it does give
off infrared energy. Scientists identify protostars within nebulae
using infrared telescopes. A protostar eventually becomes hot
enough for nuclear fusion to take place in its core. When
nuclear fusion produces great amounts of energy, a star comes
to life.
Stars begin their life cycle with different masses. A star's mass
determines how long its life cycle will last and how it will die.
Stars with a mass less than five times that of the sun are called
low-mass stars. Most stars are in this group.
A low-mass star begins its life cycle as a main-sequence star.
Over a period of billions of years, its supply of hydrogen is
slowly changed by nuclear fusion into helium. During this time,
the star changes very little.
< previous | next >
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Appendix G: Reading Comprehension Quiz

8/10/2014 Stars Reading Comprehension Quiz
https://docs.google.com/forms/d/1uHif77G6GGMuUv2RG9lHrqqnXzBGUxKPwL-wcEoOzbw/viewform 1/3
S t a r s R e a d i n g C o m p r e h e n s i o n Q u i z
* R e q u i r e d
W h a t i s y o u r 4 - d i g i t I D N u m b e r ?
1 . W h a t i s t h i s p a s s a g e m a i n l y a b o u t ? *
t h e d i f f e r e n c e b e t w e e n a p r o t o s t a r a n d a n e b u l a
t h e d i f f e r e n c e b e t w e e n l o w - m a s s a n d h i g h - m a s s s t a r s
h o w s t a r s a r e b o r n
w h y s t a r s h a v e d i f f e r e n t m a s s e s
n o n e o f t h e a b o v e
2 . H o w l o n g i s t h e l i f e c y c l e o f t h e s u n ? *
1 b i l l i o n y e a r s
1 0 b i l l i o n y e a r s
5 0 b i l l i o n y e a r s
n o o n e k n o w s
n o n e o f t h e a b o v e
3 . W h a t i s a n e b u l a ? *
a c l o u d o f g a s a n d d u s t
t h e b i r t h p l a c e o f s t a r s
t h e p h a s e o f a s t a r t h a t c a n r e s u l t i n t h e f o r m a t i o n o f a p r o t o s t a r
a c o l l e c t i o n o f h y d r o g e n , h e l i u m a n d d u s t
a l l o f t h e a b o v e
4 . W h y d o n e b u l a e c o l l e c t m o r e d u s t a n d g a s a s t h e y m o v e t h r o u g h s p a c e ? *
5 . W h a t i s a p r o t o s t a r ? *
t h e l i f e c y c l e o f a s t a r
t h e s u n t h a t g o e s w i t h a s t a r
t h e c o l d e s t p a r t o f t h e n e b u l a
E d i t t h i s f o r m
Running Head: NSTUDY FOR ADHD 117

8/10/2014 Stars Reading Comprehension Quiz
https://docs.google.com/forms/d/1uHif77G6GGMuUv2RG9lHrqqnXzBGUxKPwL-wcEoOzbw/viewform 2/3
a n i n f r a r e d t e l e s c o p e
n o n e o f t h e a b o v e
6 . I f a p r o t o s t a r d o e s n ' t g i v e o f f l i g h t , h o w d o s c i e n t i s t s k n o w i t e x i s t s ? *
7 . W h a t i s t h e f i n a l a c t i o n t h a t c a u s e s a p r o t o s t a r t o b e c o m e a s t a r ? *
n u c l e a r f u s i o n a n d a d e c r e a s e i n e n e r g y p r o d u c t i o n
n u c l e a r f u s i o n a n d a n i n c r e a s e i n e n e r g y p r o d u c t i o n
g r a v i t y s e p a r a t e s t h e c l o u d , m a k i n g i t l e s s d e n s e l y p a c k e d
a s c i e n t i s t i d e n t i f i e s i t
a l l o f t h e a b o v e
8 . W h a t d e t e r m i n e s h o w l o n g a s t a r w i l l l i v e ? *
i t s m a s s
i t s t e m p e r a t u r e
h o w c l o s e i t i s t o E a r t h
h o w c l o s e i t i s t o t h e s u n
a l l o f t h e a b o v e
9 . W h y i s g r a v i t y c r u c i a l t o t h e b i r t h o f a s t a r ? *
1 0 . I n a l o w - m a s s s t a r , w h a t d o e s h y d r o g e n c h a n g e i n t o ? *
l i g h t
d u s t
a n e b u l a
h e l i u m
a l l o f t h e a b o v e
Running Head: NSTUDY FOR ADHD 118

Appendix H: Vanderbilt ADHD Diagnostic Parent Rating Scale

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Appendix I: nStudy Training Pages

8 / 10 / 20 1 4 H i g h l i ght i n g
h t t p: / / p e r s pe c t i ve s e d . c o m / ns t u d y- v 1 / ns t u dy- 1 .ph p 1/ 1
Highlighting
Before 1800, there were very few ways to travel. People, for the
most part, relied on horse-drawn carriages. Many people
helped to invent the car. Some say French inventor Nicolas-
Joseph Cugnot built the world's first automobile. In 1769, he
invented a steam tractor. It was a self-propelled vehicle that
could travel only 2.5 miles per hour.
There are several types of cars on the roads today. Most cars
are gas-powered vehicles. Some are hybrid cars that run on
gas and electricity. There are also a few car models that run
only on electricity.
Choose a sentence from the text above and highlight it. (It
doesn't matter which one you choose. You are only
practicing in order to make sure you know how to highlight
in this program.) Then hold down the control key and click.
Select "quote" and hit Enter. Your quoted material will
appear in the panel to the left as a bookmark..
next >
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8/10/2014 Making a note
http://perspectivesed.com/nstudy-v1/nstudy-2.php 1/1
Making a note
Before 1800, there were very few ways to travel. People, for the
most part, relied on horse-drawn carriages. Many people
helped to invent the car. Some say French inventor Nicolas-
Joseph Cugnot built the world's first automobile. In 1769, he
invented a steam tractor. It was a self-propelled vehicle that
could travel only 2.5 miles per hour.
There are several types of cars on the roads today. Most cars
are gas-powered vehicles. Some are hybrid cars that run on
gas and electricity. There are also a few car models that run
only on electricity.
Highlight the definition of windshield in the image below.
Then hold down the control key and click. Select "quote
and annotate" to create a note. On that note, write one fact
you know about what a windshield does and hit Enter. You
can see your note in the section on the left under "Recent
bookmarks."
<
previous | next >
Windshield - a clear
screen (as of glass)
in front of the riders
of a vehicle
Running Head: NSTUDY FOR ADHD 123

8/10/2014 Looking up Unfamiliar Words
http://perspectivesed.com/nstudy-v1/nstudy-3.php 1/1
Looking up Unfamiliar Words
Before 1800, there were very few ways to travel. People, for the
most part, relied on horse-drawn carriages. Many people
helped to invent the car. Some say French inventor Nicolas-
Joseph Cugnot built the world's first automobile. In 1769, he
invented a steam tractor. It was a self-propelled vehicle that
could travel only 2.5 miles per hour.
There are several types of cars on the roads today. Most cars
are gas-powered vehicles. Some are hybrid cars that run on
gas and electricity. There are also a few car models that run
only on electricity.
Click on the word "propelled" above. For each blue
underlined word, a glossary page will appear that will
contain definitions of many words in alphabetical order.
< previous | next >
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8 / 10 / 20 1 4 T a g gi n g
h t t p: / / p e r s pe c t i ve s e d . c o m / ns t u d y- v 1 / ns t u dy- 4 .ph p 1/ 1
Tagging
Before 1800, there were very few ways to travel. People, for the
most part, relied on horse-drawn carriages. Many people
helped to invent the car. Some say French inventor Nicolas-
Joseph Cugnot built the world's first automobile. In 1769, he
invented a steam tractor. It was a self-propelled vehicle that
could travel only 2.5 miles per hour.
There are several types of cars on the roads today. Most cars
are gas-powered vehicles. Some are hybrid cars that run on
gas and electricity. There are also a few car models that run
only on electricity.
You can create a "tag" to categorize information for
yourself. Highlight a section of text that has to do with the
history of cars. Create that tag by holding down the control
key, clicking and selecting "Create tag." Type in "History of
Cars" and hit Enter.
< previous | next >
Running Head: NSTUDY FOR ADHD 125

8/10/2014 Searching the Internet for Information
http://perspectivesed.com/nstudy-v1/nstudy-5.php 1/1
Searching the Internet for
Information
Before 1800, there were very few ways to travel. People, for the
most part, relied on horse-drawn carriages. Many people
helped to invent the car. Some say French inventor Nicolas-
Joseph Cugnot built the world's first automobile. In 1769, he
invented a steam tractor. It was a self-propelled vehicle that
could travel only 2.5 miles per hour.
There are several types of cars on the roads today. Most cars
are gas-powered vehicles. Some are hybrid cars that run on
gas and electricity. There are also a few car models that run
only on electricity.
Highlight the term "steam tractor" above. Let's say you
wanted to know a bit about what that is, and maybe even
see a picture of it. To search the Internet for information
about steam tractors, simply click on the + sign near the
top of the screen to open a new tab in Firefox. The link for
any site you visit will appear on the pane to the left as a
bookmark.
< previous | next >
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Appendix J: nStudy Quick Reference Guide

To Highlight . . .

-‐ select text
-‐ control + click
-‐ Quote

To create a Note . . . -‐ select text
-‐ control + click
-‐ Quote & Annotate

To create a Tag . . . -‐ select text
-‐ control + click
-‐ Create tag

To go to the Glossary . . . -‐ Click on the purple underlined word
To visit another Website . . . -‐ Click on the + sign at the top of the
screen to open a new tab in Firefox

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Appendix K: Self-monitoring and Enjoyment Prompt Pages

8/10/2014 Stars 2
http://perspectivesed.com/nstudy-v1/nstudy-7.php 1/1
Congratulations! You have completed Star Formation - Part 1.
Only one more part to go. Before you begin Part 2, we have a
question and a reminder for you. First, the question:
How much are you enjoying the material you are reading about
stars? Please move the button on the slider below to indicate
your level of enjoyment and click submit:
Do not enjoy it at all Enjoy it a lot s u b m i t
To review all your bookmarks and notes, click "Open all items"
in the pane to the left. Click on the tiny arrows to see your notes.
Now for the reminder. Do you remember the five study tools you
were shown how to use before you started the passage? They
are:
1. highlighting
2. creating a note
3. selecting a tag to attach to some text
4. going to a glossary to look up an unfamiliar word
5. visiting a web page to get clarification or more information
Have you been using them? Remember, you will not be able to
go back to re-read the passage, but you will be able to see and
study your notes and bookmarks before the quiz.
< previous | next >
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8/10/2014 Stars 5
http://perspectivesed.com/nstudy-v1/nstudy-9.php 1/1
Congratulations! You have completed both parts of Formation
of a Star. Before you take your test, we would like to know how
much you have enjoyed reading this material about stars.
Please move the button on the slider below to indicate your
level of enjoyment and click submit:
Do not enjoy it at all Enjoy it a lot s u b m i t
You may now review for your test for up to five minutes if you
would like. You may review your highlights, notes and tags, but
you may not make any new ones or re-read the passage. Let us
know when you are ready to take the test.
< previous
Running Head: NSTUDY FOR ADHD 129

Appendix L: Parent Survey

8/10/2014 Online Survey Software | Qualtrics Survey Solutions
https://qtrial2014.az1.qualtrics.com/SE/?SID=SV_1SoFmZFlNQ8SXlz&Preview=Survey&BrandID=usceducation 1/2
Public
Charter
Religious
Independent (private)
Home school
Other - Please name school
Yes. He/she either uses cash or a card or a card on which I put money.
No. He/she is eligible for free lunch at school.
Other
White/Caucasian
African American
Latino
Asian
Native American
Pacific Islander
Mixed
Other
What is the 4-digit ID Number given to you?
What kind of school does your child attend?
Is your child required to pay for lunch at school?
Which of the following best describes your child's race or ethnicity?
Running Head: NSTUDY FOR ADHD 130

8/10/2014 Online Survey Software | Qualtrics Survey Solutions
https://qtrial2014.az1.qualtrics.com/SE/?SID=SV_1SoFmZFlNQ8SXlz&Preview=Survey&BrandID=usceducation 2/2
Male
Female
Other
English
A language other than English - please write it here:
No
Yes - please describe
Not sure
Yes, ADHD, inattentive type
Yes, ADHD, hyperactive type
Yes, combined type
Yes, but not sure what type
No
Not sure
Yes
No
Not sure
What is your child's gender?
What is the language your family speaks most at home?
Has your child ever received training in learning strategies, either at school or
privately?
Has your child ever been diagnosed with ADHD?
Has your child ever been diagnosed with a learning disorder?
>>
Running Head: NSTUDY FOR ADHD 131

8/10/2014 Online Survey Software | Qualtrics Survey Solutions
https://qtrial2014.az1.qualtrics.com/SE/?SID=SV_1SoFmZFlNQ8SXlz&Preview=Survey&BrandID=usceducation 1/2
If you answered "yes" or "not sure" to the previous question, please write the name
of the learning disorder or disorders with which your child has been diagnosed as best
as you know them.
Please respond to the following questions about your child's behavior. Each rating should
be considered in the context of what is appropriate for the age of your child.

0 = Never 1 = Occasionally 2 = Often 3 = Very often
Does not pay attention to
details or makes careless
mistakes, such as on homework

Has difficulty sustaining
attention to tasks or activities

Does not seem to listen when
spoken to directly

Does not follow through on
instruction and fails to finish
schoolwork (not due to
oppositional behavior or failure
to understand)

Has difficulty organizing tasks
and activities

Avoids, dislikes, or is reluctant
to engage in tasks that require
sustained mental effort

Loses things necessary for
tasks or activities (school
assignments, pencils, books)

Is easily distracted by
extraneous stimuli

Is forgetful in daily activities
Fidgets with hands or feet or
squirms in seat

Leaves seat when remaining
seated is expected

Running Head: NSTUDY FOR ADHD 132

8/10/2014 Online Survey Software | Qualtrics Survey Solutions
https://qtrial2014.az1.qualtrics.com/SE/?SID=SV_1SoFmZFlNQ8SXlz&Preview=Survey&BrandID=usceducation 2/2
Runs about or climbs
excessively in situations when
remaining seated is expected

Has difficulty playing or
engaging in leisure activities
quietly

Is "on the go" or often acts as
if "driven by a motor"

Talks too much
Blurts out answers before
questions have been completed

Has difficulty waiting his or her
turn

Interrupts or intrudes on others
(e.g. butts into conversations
or games)

>>

Abstract (if available)

Abstract This study uses the operationalization of self‐regulated learning “as an event” to analyze the frequency and type of learning strategy use among students with and without ADHD. An observational design was used to capture a snapshot of seventh‐ and eighth‐grade students’ behavior in an authentic study‐for‐a‐test learning context

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Asset Metadata

Creator Saloun, Nicole Karolina (author)

Core Title Learning tactic use among middle school students with and without ADHD in an online environment

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 03/02/2015

Defense Date 08/20/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag ADHD,educational technology,learning strategies,learning tactics,middle school,OAI-PMH Harvest,self‐monitoring,self‐regulated learning,self‐regulation,self‐report,study strategies

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sinatra, Gale M. (committee chair), Keim, Robert G. (committee member), Yates, Kenneth A. (committee member)

Creator Email Karolina@Perspectivesed.com,karolinasaloun@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-537591

Unique identifier UC11297823

Identifier etd-SalounNico-3213.pdf (filename),usctheses-c3-537591 (legacy record id)

Legacy Identifier etd-SalounNico-3213.pdf

Dmrecord 537591

Document Type Dissertation

Format application/pdf (imt)

Rights Saloun, Nicole Karolina

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA