AN EVALUATION OF THE EFFECTS OF TEACHING STUDENTS IN A
RESOURCE CLASSROOM A SELF-REGULATED ASSIGNMENT
ATTACK STRATEGY
by
BRYAN NESS
A DISSERTATION
Presented to the Department of Special Education
and Clinical Sciences
and the Graduate School of the University of Oregon
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
June 2009
11
University of Oregon Graduate School
Confirmation of Approval and Acceptance of Dissertation prepared by:
Bryan Ness
Title:
"An Evaluation of the Effects of Teaching Students in a Resource Classroom a Self-Regulated
Assignment Attack Strategy"
This dissertation has been accepted and approved in partial fulfillment of the requirements for
the Doctor of Philosophy degree in the Department of Special Education and Clinical Sciences
by:
McKay Sohlberg, Chairperson, Special Education and Clinical Sciences
Robert Horner, Member, Special Education and Clinical Sciences
Richard Albin, Member, Special Education and Clinical Sciences
Bonnie Todis, Member, Not from U of 0
Thomas Dishion, Outside Member, Psychology
and Richard Linton, Vice President for Research and Graduate Studies/Dean of the Graduate
School for the University of Oregon.
June 13, 2009
Original approval signatures are on file with the Graduate School and the University of Oregon
Libraries.
© 2009 Bryan Michael Ness
iii
Bryan Michael Ness
An Abstract of the Dissertation of
for the degree of
IV
Doctor of Philosophy
in the Department of Special Education and Clinical Sciences
to be taken June 2009
Title: AN EVALUATION OF THE EFFECTS OF TEACHING STUDENTS IN A
RESOURCE CLASSROOM A SELF-REGULATED ASSIGNMENT ATTACK
STRATEGY
Approved:
McKay Moore Sohlberg, PhD
Students who struggle academically frequently lack or are unable to apply
cognitive-motivational processes imperative for self-regulated learning. It is increasingly
evident that deficits in self-regulation are a shared characteristic across students who
qualify for special education. For example, impaired executive functions, or the cognitive
processes responsible for managing and directing goal-directed activity, is a prevalent
symptom domain across students with diverse special education identifications. Higher-
order cognitive deficits become increasingly relevant as students progress to the
secondary level as this transition necessitates the use of more complex organizational
schemes to manage increased academic workload. Assignment management is a
particularly challenging task for these students as poor organizational skills and lack of
strategic approach translate to excessive time spent on assignments, lost materials, and
negative attitude toward academic work.
There is a growing body of research suggesting interventions targeting self-
regulation have potential to improve performance on complex academic tasks such as
assignment completion. Broadly speaking, these interventions are referred to as "self-
management" strategies and are intended to help students actively reflect on their own
cognitive and learning processes while engaged in academic tasks.
The purpose of this study was to evaluate the impact of self-management strategy
instruction on student "Assignment Attack" and related academic, behavioral, and
cognitive variables. This study extended a line of inquiry examining implementation and
efficacy of interventions targeting organized, independent student completion of
assignments in resource contexts for adolescents who qualify for special education.
Utilizing a multiple baseline, across participants research design, this study revealed
positive effects of a Self-Regulated Assignment Attack Strategy (SAAS) on assignment
attack and teacher-reported student behavior during assignment completion. The results
indicated the effect was domain specific with minimal generalized improvement to the
other academic, behavioral, or cognitive outcome measures.
A discussion of the results is provided focused on the implications of improved
assignment attack, generalization, and measurement challenges. Suggestions for further
research in this area are provided.
v
CURRICULUM VITAE
NAME OF AUTHOR: Bry~n Ness
PLACE OF BIRTH: Anaheim, California
DATE OF BIRTH: July 24,1976
GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED:
University of Oregon. Eugene, Oregon.
University of Nevada, Reno. Reno, Nevada.
Calvary Chapel Bible College. Murrieta, California.
DEGREES AWARDED:
Doctor of Philosophy in Communication Disorders and Sciences, 2009,
University of Oregon.
Master of Science in Speech Pathology and Audiology, 2002, University of
Nevada.
Bachelor of Science in Speech Pathology and Audiology, 2000, University of
Nevada.
AREAS OF SPECIAL INTEREST:
Cognitive Rehabilitation
Executive Function Impairment
Cognitive Strategy Training for Adolescents
Psychosocial Impact of Traumatic Brain Injury
Vi
Vll
PROFESSIONAL EXPERIENCE:
2002-2005 Speech-Language Pathologist, Washoe Medical Center, Reno, Nevada.
2005-2009 Graduate Teaching Fellow, Communication Disorders and Sciences,
University of Oregon.
2006-2009
2007-2009
Guest Lecturer, Communication Disorders and Sciences, University of
Oregon.
Instructor (summer term) Motor Speech Disorders; University of Oregon.
GRANTS, AWARDS AND HONORS:
Graduate Teaching Fellowship. University of Oregon. 2005-Present
Clare Wilkins Chamberlin Memorial Award. University of Oregon. 2007-2008
Leon Culbertson Scholarship. University of Oregon. 2007-2008
Kasey Elizabeth Lindeleaf Scholarship. University of Oregon. 2005-2006
Jerry & Betty Wilson Scholarship in Speech Pathology. University ofNevada, Reno.
2000-2002
PUBLICATIONS:
Ness, B., & Sohlberg, M.M., Albin, R. (2008). An evaluation of a second tier
classroom-based assignment completion strategy for middle school students in a
resou,rce room context. Submitted to Journal ofRemedial and Special Education.
V111
ACKNOWLEDGMENTS
I wish to express my sincere appreciation to Dr. McKay Moore Sohlberg for her
support, wisdom, and tireless commitment to this project and every endeavor I undertook
during my doctoral program. I also thank the members of my dissertation committee for
your collective and individual contributions to this project. I immensely appreciate the
efforts of M~ry Feusahrens for her help with data collection. Finally, I wish to thank Ms.
Jennifer Martin and the Bethel School District, whose commitment to expanding
evidence-based interventions for students who qualify for special education made this
study possible.
IX
Dedicated to my loving wife and her undying support, encouragement, and patience; to
my beautiful daughter who is my inspiration; to the Lord who is my Joy and Strength.
xTABLE OF CONTENTS
Chapter Page
I. INTRODUCTION 1
Statement of the Problem 2
A Potential Solution 4
Purpose Statement.......................................................................... 6
II. REVIEW OF THE LITERATURE 7
Assigmnent Management in Secondary Education Required for 7
Advancement .
Social Cognitive Theory: A Model of Learning 10
Self-Regulated Learning................................................................................ 17
Struggling Learners and Self-Regulation ;.................................... 21
Intervention for Deficits in Self-Regulation :.......................... 27
Research Questions
III. METHODOLOGY
38
40
Pilot Study 40
Pilot Methods............................................................................................ 41
Pilot Results and Conclusions 42
Dissertation ;.................................... 45
Participants 45
Research Design 49
----~------- - --_ .._---- ... _----_.. _...
Xl
Chapter
Intervention Package: Self-Regulated Assignment Attack Strategy
(SAAS) .
Dependent Variables .
Pr.ocedures ; .
Data Collection and Analysis : .
IV. RESlTLTS .
Implementation .
Assignment Attack .
Generalized Impact .
V. DISCUSSION .
Fidel~ty of Implementation in Natural Context ..
Improved Assignment Attack · .
Generalized Impact .
Lil)1itations and Future Directions .
Summary .
Page
51
58
69
70
74
74
78
82
95
96
97
99
103
104
APPE]~DICES 105
A. TEACHER SCRIPT FOR INITIAL INSTRUCTION 105
B. TEACHER SCRIPT FOR DAILY PROMPTS 107
C. TEACHER SCRIPT FOR FADING 4P'S CHECKLISr.......................... 108
BIBLIOGRAPHY 109
xu
LIST OF FIGURES
Figure Page
1. Assignment Attack Rating Scale 43
2. The Four P's Checklist 52
3. Self-Regulated Strategy Development Stages 54
4. Modified Checklist 57
5. Homework Problem Checklist 60
6. Student Self-Regulation Probe 63
7. Treatment Fidelity Worksheet 65
8. Social Validity Measure for the Teacher 67
9. Social Validity Measure for Students 68
10. Assignment Attack Data 80
11. Percentage ofIntervals On Task 91
X111
LIST OF TABLES
Table Page
1. Demographic Data 48
2. Summary of Study Results ;.................................... 75
3. Student Social Validity Data :.......................... 77
4. Teacher Social Validity Data........................................................................... 78
5. Classroom Grades and Assignment Completion Rate 84
6. Self-Regulated Learning Outcome Data and Analysis 86
7. BRIEF Outcome Data and Analysis 88
8. Homework Problem Checklist Data 89
9. Correlation Coefficients for Assignment Attack Domains and On-Task 92
CHAPTER I
INTRODUCTION
"Give a man a fish and you feed him for a day. Teach a m~n to fish and you feed
him for a lifetime." (Anonymous) The ancient proverb succinctly illustrates the
motivation of educational researchers endeavoring to equip students with the tools
necessary for effective learning. Teaching students to become self-sufficient learners has
become increasingly important as educational attainment is closely linked to future
opportunity for young people. At a time when students of all learning abilities are taught
and evaluated under similar standards, it is imperative that educational professionals
understand the process of effective learning and have the tools to teach all students to
become self-sufficient in the classroom.
This dissertation examines potential learning and academic effects from the
implementation of a cognitive strategy intervention designed to help struggling students
self-regulate their learning. The intervention is grounded in social-cultural theory. The
mechanisms of learning as a developmental process have been a point of philosophical
and theoretical debate for decades. Most notable are Piaget's learning stages model,
Vygotsky's social-cultural perspective and "zone of proximal development," and
Bandura's social-cognitive theory. Each ofthese theories has left an indelible mark on
1
2education, but social-cognitive theory purports the most comprehensive explanation of
the cognitive processes linked to efficient learning.
Social-cultural theory proposes that learning results from interacting with the
environment-and is shaped by inter-related motivational, behavioral, and cognitive
processes (e.g., Bandura, 1986; Boekaerts & Cascallar, 2006; Scliunk & Zimmerman,
1997). One critical cognitive process essential for learning and academic achievement is
self-regul~tion. Self-regulation refers to the capacity for evaluating and controlling
thought processes and behavior based on situational demands (Boekaerts, Maes, &
Karoly, 2005). Applied to educational contexts, students self-regulate their learning by
continually assessing their cognitive and motivational state during academic tasks,
making adjustments through the use of strategies (Zimmerman, 1990). The degree to
which students self-regulate their learning is closely related to higher academic
achievement (Pintrich & DeGroot, 1990).
Statement ofthe Problem
Students who struggle academically frequently lack or are unable to apply the
cognitive-motivational processes imperative for self-regulated learning (e.g., Happe,
Booth, Charlton, & Hughes, 2006). The largest proportion of research examining self-
regulation and struggling learners concentrates on students identified with learning
disability, attention-deficitlhyperactivity disorder, and autism (e.g., Denckla, 1996; Hill,
2004). It is increasingly evident that impaired self-regulation processes are a shared
characteristic across these students. For example, impaired executive functions, or the
cognitive processes responsible for managing goal-directed activity, is a prevalent finding
3across students who qualify for special education (Pennington & Ozonoff, 1996; Willcutt,
Pennington, Olson, Chabildas, & Hulslander, 2005).
The number of students who exhibit difficulty with self-regulated learning is
large. There are over 6 million students enrolled in special education, and the number of
students in special education has increased by over 20% in the last 10 years (National
Center of Educational Statistics, 2006). According to the U.S. Department of Education,
nearly 3 million students at the secondary education level receive special education
services under IDEA legislation, half of whom are identified with learning disabilities,
ADHD ("Other health" category), and autism (Office of Special Education Programs,
2007).
Poor self-regulated learning is magnified as students advance to secondary
education. The transition out of elementary school necessitates the use of more complex
organizational schemes to manage increased academic workload (Ylvisaker & DeBonis,
2000). Additionally, students must cope with increased expectatiQns of autonomy and
varied teachers' expectations demanding implementation of flexible cognitive-
motivational strategies (Lord, Eccles, & McCarthy, 1994; Rudolph, Lambert, Clark, &
Kurlakowshy, 2001). An example of an academic skill that requires self-regulation is
assignment management. The vast majority of students at the secondary education level
are expected to complete assignments outside the classroom independent of teacher
support (Gajria & Salend, 1995; Struyk & Epstein, 1995). Students who struggle
academically often lack organizational skills and strategic approach to managing
4assignments resulting in excessive time spent on assignments, lost materials, and negative
attitude toward out-of-class academic work (Bryan, Burstein, & Bryan, 2001).
Independent assignment completion is particularly critical to academic success
(Cooper, Robinson, & Patall, 2006). The majority ofteachers assign homework and use
assignment completion for evaluation purposes (Munk et at, 2001; Struyk & Epstein,
1995). In addition, completing assignments independently supports mastery of academic
concepts. Research demonstrates students who complete more assignments outside the
classroo~ achieve higher academic success (Cooper et aI., 2006; Trautwein & Koller,
2003; Trautwein, Koller, Schmitz, & Baumert, 2002). The proposed mechanism
explaining the effect of assignment completion is that students benefit from more time
spent engaged in academic concepts (Cooper, Lindsay, Nye, & Greathouse, 1998;
Trautwein, Ludtke, Schnyder, & Niggli, 2006).
A Potential Solution
Researchers grounded in social-cognitive theory have developed and evaluated
interventions targeting self-regulation to improve performance on complex academic
tasks such as assignment completion. The most documented approaches for teaching
students to self-regulate their learning fall under the broad label of self-management
strategies (Belfiore & Hornyak, 1998). Self-management approaches focus on helping
students actively reflect on their own learning using techniques such as "self-instruction,"
self-evaluation, and following task-specific checklists (Belfiore & Hornyak, 1998; Reid,
1996). These approaches have been utilized with students who qualify for special
education with positive effects seen on academic tasks such as reading comprehension,
5homework completion, and solving math problems (e.g., Shimabukuro, Prater, Jenkins, &
Edelen-Smith, 1999; Uberti, Mastropieri, & Scruggs, 2004).
Two recent systematic reviews of self-management literature documented the
effectiveness of these interventions for students who qualify for special education. Reid
and colleagues (2005) evaluated the effects of four strategy instruction approaches (self-
monitoring, self-monitoring plus reinforcement, self-reinforcement, and self-
management) on academic behavior for students identified with ADHD. Mooney and
colleagues (2005) published a similar synthesis of self-management interventions for
students identified with emotional behavioral disorders. Both of these meta-analyses
demonstrated consistent positive effects of self-management strategy instruction on a
variety of academic skills including reading comprehension, on-task behavior, and
academic productivity (Mooney, Ryan, Uhing, Reid, & Epstein, 2005; Reid, Trout, &
Schartz, 2005).
While evidence supporting the use of self-management approaches to support
academic tasks is increasing, there are gaps in the literature. For example, while it is
evident that teaching students self-regulation strategies positively affects the target
behavior (e.g., Graham, Harris, & Mason, 2005; Harris, Friedlander, Saddler, Frizzelle, &
Graham, 2005), little is known about the broader impact of these approaches on other
cognitive-motivational domains such as executive functions. Also, since self-regulation
interventions are applied in school settings, implementation is a vital aspect of any
approach but little is known about key aspects that make strategies easy to apply in
classroom se~tings.
6Purpose Statement
The purpose of this study was to examine the effects of an assignment completion
strategy on academic, cognitive, and motivational variables for students who qualify for
special education. This study extended the literature base in two ways. First, this study
evaluated both the direct and indirect effects of self-management strategy instruction.
Second, this study validated practices related to strategy implementation including
teacher preference for presentation mode and teaching strategies at the classroom versus
individual level.
T~e rationale, procedures, and results for this study are outlined in the following
chapters. Chapter Two ofthis dissertation provides an overview of effective learning
processes based on social-cognitive theory. The importance of managing academic tasks
relative to academic achievement is addressed followed by a description of the target
population for this study. Next, a review of current interventions targeting self-regulation
is provided. Chapter Three outlines the research methods utilized in this study including
descriptions of intervention materials, measures, procedures, and data analysis plan.
Chapter Four provides a summary of the results, and Chapter Five concludes this
dissertation with a discussion of the findings.
7CHAPTER II
REVIEW OF THE LITERATURE
Assignment Management in Secondary Education Requiredfor Advancement
When students advance beyond elementary school, key cognitive-academic needs
emerge (Rudolph et aI., 2001). These requirements include more advanced organizational
schemes, planning skills, and abstract thinking (Ylvisaker & DeBonis, 2000). Students
must function more autonomously in secondary education and take responsibility for
their own learning (Valiente, Lemery-Chalfant, Swanson, & Reiser, 2008). Successful
advancement requires stude!1ts to develop effective strategies for managing academic
tasks.
A critical required academic task in secondary education is completing
assignments. Students are expected to complete assignments at home and in class
independently and assume more personal responsibility for their learning (Harter, 1992).
Most students are assigned homework several nights a week at the secondary education
level (Munk et aI., 2001; Struyk & Epstein, 1995). Completing out-of-class assignments
leads to increased academic competence, and homework is often included in classroom
evaluation (Cooper et aI., 2006).
The primary benefit of out-of-class assignments is providing students
opportunities to spend time individually engaged in academic concepts (Cooper et aI.,
82006). Trautwein and colleagues (2002) conducted a longitudinal study of 1,976 students
in Germany examining the relationship between student characteristics, classroom
characteristics, homework practices and academic achievement. They reported that
students who spent time engaged in out-of class assignments, especially when the tasks
were challenging, obtained higher academic achievement.
Homework Management
Homework is defined as tasks assigned by teachers to learners with the intent the
assignment is completed outside the classroom without teacher assistance (Cooper,
1989). There are two general reasons for assigning homework. First, teachers design
homework to provide students opportunity to review and practice material that has been
learned in school. Second, assignments can provide opportunity to amplifY or enrich
previously learned material (Hong & Milgram, 1999). The tasks are usually more time
consuming than in-class work and focus on helping students process the material (Hong
& Milgram, 1999). Assignments may include tasks that are completed daily (e.g., math
worksheets) ~r longer projects targeting information synthesis (e.g., book reports, term
projects, or science experiments). Numerous studies have established the importance of
time spent engaged in independent studying on academic outcome; students must
independently manage these assignments to benefit from the additional practice (Cooper,
Jackson, Nye, & Lindsay, 2001; Cooper et aI., 2006).
Homework completion is a process that requires students to manage an array of
personal and contextual factors. Cooper's (1989) homework process model outlines five
factors that influence the assignment completion process: exogenous factors, assignment
9characteristics, initial classroom factors, home-community factors, and classroom follow-
up. Exogenous factors are student characteristics (i.e., ability, motivation, and habits),
subject matter, and grade level. Assignment characteristics include length, purpose, and
skill area. Initial classroom factors are provision of materials and facilitators (i.e.,
suggested approaches, links to curriculum, and "other rationales"). Home-community
factors are competitors for student time (e.g., sports, television, chores), home
environm~nt (e.g., space, noise, materials), and others involvement (i.e., parents, siblings,
others). Finally, classroom follow-up factors are feedback, testing of related content, and
use in class discussion (Cooper, 1989; Cooper & Nye, 1994).
Managing homework involves self-directed cognitive and motivational processes
(Cooper, 1989). Students must have the capacity to recall assignments, gather materials,
initiate assignments, and remain engaged without direct teacher supervision. Completing
assignments also requires motivation since there is often competition for students' time
and attention (Wolters, 1999).
In-class Assignment Management
In addition to homework, students are more responsible for regulating their
classroom learning as they advance academically (Paris & Paris, 2001). There is more
autonomy in secondary education classrooms that necessitates more student control over
the learning process (Valiente et aI., 2008). Students who approach their classroom
learning systematically are more likely to succeed academically (Pintrich & DeGroot,
1990; Zimmerman, 1990).
10
In-class work is most commonly prescribed to help students practice concepts
already learned in the curriculum (Struyk & Epstein, 1995). Classroom activities
commonly include writing exercises, drill work, and reading comprehension tasks
(Kameemii & Simmons, 1990). To complete these tasks independently, students must be
skilled at setting task-oriented goals, selecting and applying cognitive-academic
strategies, and self-monitoring their learning process in the classroom (Boekaerts &
Cascallar, 2006; Boekaerts, Koning, & Vedder, 2006; Reid et aI., 2005).
The transition to secondary education is accompanied with increased expectations
for independent academic functioning. Students who succeed aca~emically, manage both
in-class and out-of-class academic tasks efficiently (Cooper et aI., 2006). There are many
interacting variables that affect independent assignment completion, hence powerful
learning processes are necessary to accomplish the task. The next section of this paper
provides a brief overview of the cognitive-motivational processes involved in typical
learning.
Social Cognitive Theory: A Model ofLearning
The leading theory explaining how students acquire complex tasks such as
assignment management is social cognitive theory. Social cognitive theory provides a
comprehensive framework explaining learning (Bandura, 1986). According to this theory,
the extent to which learning takes place is dependent on the interaction of behavioral,
environmental, and cognitive factors.
11
Role ofBehavioral Principles in Learning
Behavior in learning contexts refers to both level of effort and use of strategies
(Boekaerts & Cascallar, 2006). Behavioral influences are best understood from traditional
behaviorist theory, which attributes learning to positive reinforcement for academic
performance. Students are more likely to put more effort into tasks they previously found
rewarding. Behavior that supports learning is therefore shaped by reinforcement
(Wolters, 1999).
Students who learn and use learning strategies successfully learn to apply similar
strategies in other contexts (Hattie, Biggs, & Purdie, 1996). One effective academic
strategy is note taking (Ries et aI., 2000). Students learn the benefit of note taking in one
class by experience, and this shapes their use of note taking in other classes.
Role ofEnvironment in Learning
Environment plays a significant role in learning, according to social cognitive
theory. Cogriitive development is stimulated by the presence of sensory input during the
early stages of development (Novak & Pelaez, 2004). Later in life, other environmental
factors including access to books or other educational materials, stable horne
environment, and positive peer relationships all have significant impacts on cognitive,
personality, and academic development (e.g., Hallahan et aI., 2005; Thompson, 1999; and
Spear, 2000). Access to various experiences is important to development, but the
mechanism through which these experiences contribute to knowledge acquisition is a
matter of debate.
12
Social-cognitive theorists suggest one way environmental factors impact learning
and development is through observational learning. Observational learning is the idea that
children expand their knowledge of the world by observing others perform either a task
they would either like to learn or have never done before (Novak & Pelaez, 2004).
Observational learning has been credited as one mechanism through which children learn
behaviors that are rarely taught explicitly, such as aggression (e.g., Bandura, 1977).
In academic arenas, students' knowledge and skill acquisition develop by through
observation and shared experiences with experts (Schunk & Zimmerman, 2007;
Zimmerman, 1990). Peer or teacher models are effective observational learning tools
because they. highlight key salient features and provide instruction in a relevant context
(e.g., Kameenui & Simmons, 1990). Using academic strategies, managing homework
assignments, and mastering math concepts are some skills research suggests develop by
observing expert models (Boekaerts & Como, 2006).
Role ojCognition in Learning
Social cognitive theory extends prior behaviorist theories by expounding on the
role of cognition in learning (Zimmerman, 1990). An elemental understanding of social
cognitive theory is the notion that humans have certain cognitive "capacities" (Bandura,
1986). One such capacity is "self-direction". Though individuals learn through vicarious
experience, they have control over their actions and are able to ditect their own learning.
Three additional capacities highlighted by Bandura are the capacity to self-motivate, self-
reflect, an.d self-regulate.
13
Capacity to sel.fmotivate.
Motivation is the term applied to an individual's willingness to learn or engage in
new activities (Wolters, 1999). Boekaerts and Como's (2005) dual processing model is
an illustration of how motivation processes affect student behavior in academic contexts.
This model dichotomizes motivation according to two personal pathways. The "growth
track" is a process of top-down motivation where an individual chooses to pursue
learning goals because of personal interest or expected satisfaction. These learners apply
effort because they value the goal ofleaming. The "well-being" t~ack is a bottom-up
process whereby motivation is derived from environmental stimuli. Students focus on
avoiding unfavorable learning contexts or situations. In this case, student effort is
concentrated on not completing assignments or engaging in academics because their
perceived well-being is dependent on avoiding the task (Boekaerts & Como, 2005).
The relationship between academic motivation and achievement has been
demonstrated for students in general education settings (Pintrich & DeGroot, 1990;
Wolters, 199.9). In a retrospective analysis of the National Education Longitudinal Study
(NELS; 1988), Singh and colleagues (2002) used structural equation modeling to
demonstrate the relationship between motivation and achievement in math and science
for over 2~,000 eighth grade students. Their results show statistically significant
correlations between motivation variables and achievement in math and science (Singh,
Granville, & Dika, 2002).
Motivation is important in assignment management because external motivators
(i.e., teachers or parents) are typically absent. Under this independent condition,
14
Trautwein and colleagues (2003) link Expectancy-Value theory with assignment
completion research to explain individual student motivation. This theory relates a
student's expectation of success and value ofthe task to homework behavior. Students
who expect to do well on a task are more likely to spend time on assignments. Likewise,
students who enjoy doing certain activities or who value doing well on specific tasks are
also more likely to persist with challenging work (Trautwein & Koller, 2003).
Capacity to selfreflect.
According to social cognitive theory, a potent influence on learning is the
capacity for self-reflection. The distinctly human capacity for self-awareness enables
individuals to analyze their experiences and thought processes (Prigatano, 2005). Self-
reflection describes the ability to analyze how effectively thoughts or actions help an
individual complete daily activities.
A cognitive process associated with self-reflection is meta;cognition.
Metacognition is known colloquially as the ability to think about thinking. A more formal
definition of metacognition is a " ... self-evaluative and regulatory function that
encompasses self-appraisal or monitoring of one's own cognitive processes ... " (Levin &
Hanten, 2005). Metacognition allows students to evaluate the effectiveness of their
approach to learning and monitor their progress on academic tasks (Zimmerman, 1998).
A common metacognitive process important for academics is self-monitoring
(Reid, 1996; .Reid et aI., 2005). Self-monitoring involves periodic checks of both thought
processes and academic productivity. Students who self-monitor their behavior and
learning recognize when their initial expectation (i.e., task requirements or knowledge of
15
the subject) is inaccurate and adjust their thought processes accordingly (Boekaerts,
1997). Efficient learners evaluate their own cognitive approach during academic tasks
(e.g., using learning strategies) based on how effectively the strategy facilitated their
thinking (Zimmerman, 1998).
Capacity to sel.fregulate.
Self-regulation refers to the capacity for controlling behavior using internal
standards. Self-regulation occurs when a person evaluates their actions or thoughts and
adjusts based on discrepancies between the actual and ideal behavior (Bandura, 1986).
Self-regulation is the process that allows for cognitive flexibility in the face of
environmental or motivational barriers (Boekaerts et aI., 2005). When describing self-
regulation, researchers often provide behavioral examples (see review in Zimmern1an,
1990). For instance, students self-regulate their academic behavior by delaying
gratification. Every student faces the dilemma of choosing between immediate
gratification (i.e., hanging out with friends) or delaying gratification by pursuing
activities that have long-term beneficial consequences (i.e., doing homework).
The cognitive processes closely linked to self-regulation are known as executive
functions. Executive functions are processes essential for managing complex cognitive
tasks. These processes are often personified as "managers" because they direct
subordinate domains such as memory, language, and motor skills.for accomplishing goals
(Levin & Hanten, 2005; Miller & Cohen, 2001). Common behavioral correlates to
normal executive functioning include starting or stopping behavior, task persistence,
planning, problem solving, and organization (Sohlberg & Mateer, 2001). These behaviors
16
indirectly support learning since they provide the foundation for attending to all academic
tasks.
Students who self-regulate their academics manage these processes using
cognitive strategies. Self-instruction is an example of self-regulation cognitive strategy
whereby students generate a set of instructions for completing a task (Harris, 1990). They
repeat the instructions internally to help guide them through multi-step tasks. Self-
instruction has been used to help students problem solve difficult academic tasks such as
multi-step equations in math (Wood, Rosenberg, & Carran, 1993). Self-instruction
strategies are also effective for managing maladaptive thought processes and emotions
which interfere with learning, such as anxiety (Kamann & Wong, 1993).
ImpactofseljCejTTcacy.
If students believe they are good at a task, they will exert more effort on the
activity (Bandura, Barbaranelli, Caprara, & Pastorelli, 1996). The. role of personal belief
and efficacy in academics is a central theme in social cognitive theory and has been
elaborated on by Bandura (1996) and others. Self-efficacy can be defined as an
individual's belief that they have the ability to control their level of functioning in
response to environmental demands (Bandura et aI., 1996). Academic self-efficacy
develops with experience. Positive learning experience (e.g., mastering a concept) is the
strongest predictor of high academic self-efficacy (Usher & Pajares, 2006).
Perceptions of efficacy influence the learning process. Applied to academic
contexts, Pajares (2003) summarizes the role of self-efficacy, " ... ·(efficacy) affect(s)
what students do by influencing the choices they make, the effort they expend, the
17
persistence and perseverance they exert when obstacles arise, and the thought patterns
and emoti.onal reactions they experience." Perceptions of efficacy account for students'
initial willingness to engage in new or difficult tasks (Vollmeyer & Rheinberg, 2006).
Students who feel they have a higher initial "probability of success" on a task are more
likely to complete the task effectively than students who do not (Vollmeyer & Rheinberg,
2006). Students may have high self-efficacy in certain academic domains such as math,
reading, writing (Pajares, 2003). Students can also have perceptions of self-efficacy
towards learning in general (Bandura, 1996; Usher & Pajares, 2003).
Students with high sdf-efficacy, who strongly believe in their ability to control
their own 'learning, consistently exhibit higher academic achievement than students with
low efficacy (Bassi, Steca, Fave, & Caprara, 2007). Bassi and colleagues (2007) found
that students with high academic self-efficacy devoted more time to academics outside of
school and managed stressful academic situations (e.g., studying for an exam) with less
anxiety than students with low self-efficacy. Also, students with higher academic self-
efficacy are more likely to persevere during difficult tasks or tasks for which they have
little prior experience (Schunk, 1989).
Self-Regulated Learning
Self-regulation is a psychological construct with broad application. It has been
examined from cognitive (Baddeley, 1996), behavioral (Carver & Sheirer, 1998), and
educational (Zimmerman, 1990) perspectives. The importance of self-regulation for
functioning in every life domain renders this a critical concept. The current work is
interested in .self-regulation as applied to leaming.
18
Definition ofSe?f-Regulated Learning
Examining self-regulation in educational context refers to understanding how and
why student~ govern their own learning (Zimmerman, 1990). As Zimmerman (1990)
stated, "The task of exampling why and how students assume resflonsibility for
regulating their own acquisition of knowledge and skill, often in the face of obstacles,
provides one of the most demanding tests for theories of self-regulation" (p. 184). A
recent review of self-regulation in learning provides a basic overview of the main
premises (Boekaerts & Como, 2005):
All theorists assume that students who self-regulate their learning are
engaged actively and constructively in a process of meaning generation
and that they adapt their thoughts, feelings, and actions as needed to
affect their learning and motivation. Similarly, models assume that
biological, developmental, contextual, and individual difference
constraints may all interfere with or support efforts at regulation.
Theorists are in agreement that students have the capability to make use
of standards to direct their learning, to set their own goals and sub-
goals. Finally, all theorists assume that there are no direct linkages
between achievement and personal or contextual characteristics;
achievement effects are mediated by the self-regulatory activities that
students engage to reach learning and performance goals. (p. 201)
It is critical to remember that social-cognitive theorists view self-regulation in
academics as a skill (Schunk & Zimmerman, 1997). As with other academic skills,
students learn to hone the processes through experience. With respect to educational
contexts, self-regulation develops largely through peer and teacher models, instructor
guidance, an? direct instruction (Boekaerts, 1997; Shunk &Zimmerman, 1997).
Boekaerts (1997) identified six regulatory skills students learn to apply in
academics. These skills are divided into cognitive self-regulation or motivational self-
regulation components. These six components are interwoven, affecting both student
19
effort and task performance during learning (Boekaerts, 1997). There are three cognitive
self-regulation components in learning. The first is the capacity to acquire knowledge and
access prior knowledge. Cognitive self-regulation also includes control of cognitive
strategies such as rehearsal, selective attention, and decoding. Finally, self-regulation in
learning necessitates cognitive regulatory strategies such as progress monitoring,
planning, and evaluating goal achievement. These three behaviors are evidence of self-
regulation over cognitive processes associated with learning (Boekaerts, 1997).
The other three components in Boekaerts' (1997) self-regulation model pertain to
control over motivational factors. There are three motivational self-regulation
components students manipulate while learning. The first is meta-cognitive knowledge
and motivational beliefs. Examples of these include beliefs or attitudes pertaining to the
task and belief in personal capacity to succeed at the task. Second, motivational self-
regulation includes using motivation strategies such as using social resources or creating
a learning intention. Finally, students apply motivational regulatory strategies such as
coping with stressors and considering past successes with similar tasks (Boekaerts, 1997).
The Process ofSelf-Regulated Learning
Boekaerts' model provides a framework for understanding the self-regulatory
processes involved in learning. The term self-regulated learning has evolved to describe
student application of cognitive-motivational processes toward the goal of academic
achievement (Pintrich & DeGroot, 1990). Self-regulated learners are able to make
adjustments (e.g., increase motivation, reset goals, etc.) to improve performance due to
regular performance checks (Zimmerman, 1998).
20
Zimmerman (1998) outlines a cyclical process of self-regulated learning in three
phases: (1) forethought, (2) performance, and (3) self-reflection. "Forethought"
encompasses influential processes that "set the stage" for learning. Within this phase are
the sub-processes of goal setting, strategic planning, self-efficacy beliefs, goals
orientation, and intrinsic interest. "Performance" or "volitional control" describes
processes students control during learning. These include attention, self-instruction, and
self-monitoring. Finally, the "self-reflection" phase describes the process of learner
reaction to the experience. "Self-reflection" includes the sub-processes of self-evaluation,
attributions, self-reactions, and adaptability. Students who self-regulate their learning
repeatedly engage in this open-ended process.
The Strategies ofSelf-Regulated Learning
Students' capacity to self-regulate their learning depends largely on their ability to
utilize cognitive strategies (Boekaerts, & Cascallar, 2006; Zimmerman, 1990). Cognitive
strategies help with information processing, retention, and retrieval (Gazzaniga, Ivry, &
Mangun, 2002). One example of a cognitive strategy used for reading is the key word
search strategy. Students can orient themselves to the content of reading passages before
engaging in the text by scanning for key words. This effectively reduces the cognitive
load during the task enabling students to spend more effort on comprehension. Other
reading strategies include self-questioning for comprehension and decoding (Johnson,
Graham, & Harris, 1997; Taylor, Alber, & Walker, 2002). A number of strategies target
memory processes. Common memory strategies are rehearsal and, mnemonics. Self-
21
regulated learners evaluate how effectively each strategy helped them accomplish their
goal and students develop schemas for dealing with similar tasks (Zimmerman, 1998).
The most common strategies defined in self-regulated learning literature are
metacognitive strategies (Boekaerts & Cascallar, 2006; Zito, Adkins, Gavins, Harris, &
Graham, 2007). The National Research Council (NRC) published a review of research
examining effective learning principles. One of the three critical learning processes they
identified was the capacity to self-monitor learning (NRC, 2005). Students use self-
monitoring strategies to evaluate a number of aspects relative to learning such as
motivational. influences, strategy effectiveness, attention, progress and comprehension
(Harris et aI., 2006; Reid, 1996; Zimmerman, 1998). Other metacognitive strategies
utilized by self-regulated learners include strategic planning, self-instruction, visualizing
goal attainment, and self-evaluation (Zimmern1an, 1998).
According to self-regulated learning theorists, metacognitive strategies are subject
to volitional control (Como, 2001). Students choose when and which strategies to apply
based on context. With experience, students apply strategies automatically, which is
referred to as "routinization" (Zimmerman, 1998). Some suggest that automatic strategy
use signifies 'less demand to self-monitor as the learning process becomes routine.
Students who attribute their success to certain strategies will routinize those strategies
quickly (Zimmerman, 1998).
Struggling Learners and Self-Regulation
Studies show that students who exhibit characteristics of self-regulated learning
are successful learners (see review in Zimmerman, 1998). For example, Feldman and
22
colleagues (1995) found that students who utilized self-regulatory behaviors completed
more classroom activities than their peers who did not. Similarly, students who reported
optimizing their environment and setting personal goals for completing homework were
more productive students than their peers (Feldman, Martinez-Pons, & Shaham, 1995;
Gajria & Salend, 1995). Self-regulated learning behaviors predict both overall academic
achievement (i.e., grade point average) and concept mastery (Schunk, 1998; Wolters,
1999). However, a large proportion of students are unable to manage academic tasks in
relation to increased expectations at the secondary education level. The following
overview describes these students and their difficulties self-regulating the learning
process.
Evidence ofImpaired Self-Regulation with Struggling Learners
According to the U.S. Department of Education, nearly 3 million students
between the ~ges of 12 and 17 receive special education services under IDEA legislation
(Office of Special Education Programs, 2007). These students not only struggle with
academic concepts, but also have difficulty managing the more complex learning
environment in secondary education (Lord et aI., 1994; RUdolph et aI., 2001). A striking
commonality among these students is poor self-regulation.
Struggling learners and cognitive control.
There is a growing body of research demonstrating pervasive deficits in executive
functions across students who qualify for special education under IDEA legislation
(Denckla, 1996; Hill, 2004; Pennington & Ozonoff, 1996; Rucklidge & Tannock, 2002;
Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Evidence suggests students in six
23
of the thirteen IDEA-defined disability categories exhibit some form of executive
function impairment, a critical cognitive component of self regulated learning (Anderson
& Catroppa, 2005; Geurts, Verte, Oosterlaan, Roeyers, & Sergeant, 2004; Hill, 2004;
Olvera, Semrud-Clikeman, Pliszka, & O'Donnell, 2005; Shear, DelBello, Rosenberg, &
Strakows~i, 2002; Willcutt, Pennington et aI., 2005). Intact executive functions are
required in each phase of Zimmerman's (1998) cyclical self-regulated learning model:
forethought, performance/volitional control, and self-reflection.
Happe, Booth, Charlton, and Hughes (2006) compared the executive function
profiles of children and adolescents with attention deficit-hyperactivity disorder
(ADHD), autism, and matched typically developing peers. The researchers used a
variety of cognitive tests to measure the domains of response selection, cognitive
flexibility, and planning/working memory. Group-wise and post hoc statistical analysis
revealed'that students with ADHD performed significantly worse on measures of
response selection and planning/working memory compared to students with autism or
the typically developing group.
Seidman and colleagues (2005) reported the results of a study examining higher-
order cognitive skills in adolescents identified with ADHD. The sample of204
adolescents with ADHD and 109 matched control peers were compared based on
performance on a battery of neuropsychological tests designed to sample attention,
planning, organization, response inhibition, set shifting, and memory. Adolescents with
ADHD scored lower on all tests compared to the control group, with levels reaching
24
statistical significance on measures of response inhibition and set shifting (Seidman et aI.,
2005).
One implication of reduced cognitive control for students who struggle
academically is poor use of learning strategies. Students with learning disabilities (LD)
reported lower metacognitive strategy use and lower perceived ability to self-regulate
their learning compared to their peers (Klassen, Krawchuk, Lynch, & Rajani, 2008).
They are also less skilled at self-monitoring (Harris et aI., 2005), strategic planning
(Sikora, Haley, Edwards, & Butler, 2002), and inhibiting responses to distracting stimuli
(van Mourik, Oosterlaan, & Sergeant, 2005). These traits restrict volitional control over
strategy selection and implementation in the self-regulated learning cycle (Zimmerman,
1998).
Struggling learners and motivational capacities.
There is a growing body of evidence that demonstrates motivation differences
between higl) achieving and low achieving students (Fulk, Brigham, & Lohman, 1998;
Pintrich, Anderman, & Klobucar, 1994). Students with LD are often motivated to avoid
work because of prior experiences of failure (Boekaerts et aI., 2006; Fulk et aI., 1998;
Pintrich & DeGroot, 1990). For example, one recent study of college students revealed
that those identified with LD spent significantly more time in behaviors identified as
"procrastinating" (Klassen et aI., 2008). There is also evidence that low achieving
students are less skilled at managing their academic motivation, choosing "maladaptive"
motivators more often than high achieving students (Anderman & Young, 1994). These
25
students are more likely to find motivation avoiding academic tasks and pursuing the
"well-being" track described by Boekaerts and Como (2005).
Another area where struggling students differ from high achieving students is
academic self-efficacy. There is substantial evidence that struggling students lack
confidence in their ability to succeed academically (Bandura et ai., 1996; Schunk, 1989).
These students are more likely to under- or over-estimate their ability to complete tasks
resulting in less persistence in the face of adversity (Bassi et ai., 2007; Klassen & Lynch,
2007; Pajares, 2003). These motivational variables are linked to all three phases of the
self-regulated learning cycle (Zimmerman, 1998).
Impact ofpoor self-regulation on assignment management.
Impairments in the cognitive control and motivational processes necessary for
self-regulation negatively impact assignment management. Bryan, Burstein, and Bryan
(2001) published a review of assignment management and completion problems for
students identified with LD. Their summary reveals a pattern of assignment completion
difficulties. For example, students with learning disabilities spend more time on
assignments due to poor organization and time management compared with peers. In
addition, students who struggle academically lose materials and forget assignments more
frequently resulting in less time engaged in academics (Bryan et ai., 2001).
Gajria and Salend (1995) published the results of a study examining assignment
completion problems for middle school students identified with LD. Using student
completed questionnaire data, students with LD report more problems starting and
completing homework than a group of matched control students. Examples of se1f-
26
reported problems that students with LD report and their peers do not included "forget to
bring assignments back to class", "I don't finish my work", and "when I find an
assignment too hard, I stop working on it" (Gajria & Salend, 1995).
Using a parent and teacher questionnaire, Soderlund and Bursuck (1995)
published a review of assignment completion problems at home and in school for
students identified with behavior disorders. Parents and teachers completed the
Homework ~roblemChecklist (HPC; Anesko and colleagues, 1987) for 82 students with
behavior disorders and 114 students without disabilities. The results revealed statistically
significant differences in perceived behavior differences between the two student groups.
The three items with the largest between group differences identified by teachers were
"easily distracted by noises", "responds poorly when told to do work", and "puts off
doing work". The two items differing most on parent reports were "procrastinates" and
"easily distracted" (Soderlund, Bursuck, Polloway, & Foley, 1995).
Epstein and Polloway (1993) published a comparison of special education
teacher, general education teacher, and parent perception of student assignment
management. The students in this study were identified with learning disabilities,
emotional behavior disorders, and age-matched peers. Analyzing questionnaire data, the
results re,:,ealed statistically significant differences between students identified with
disabilities compared to the control group. There were no statistically significant
differences between students in the two disability groups. Similar to previous studies, the
researchers reported problems related to poor assignment management including failure
27
to remember assignments, relying on others to help start and complete assignments, and
losing focus (Epstein & Polloway, 1993).
Intervention for Deficits in Self-Regulation
The literature documents the pervasiveness of self-regulation deficits across a
large proportion of students with special learning needs. The barriers imposed by poor
self-regulated learning restrict student capacity to engage in academic tasks
independently. Given the impact of these deficits on academic success, a number of
interventions have been proposed targeting self-regulation processes in struggling
students. Common interventions fall into three very broad domains: pharmacological,
behavioral, and cognitive.
Pharmacological interventions utilize medications that interact with
neurochemical exchange resulting in improved capacity to engage in academic tasks.
Students identified with ADHD or conduct disorder/emotional behavioral disorder are the
most common recipients of pharmacotherapy (e.g., Biedennan et aI., 1999; Kratochvil et
aI., 2002): Commonly prescribed medications include Clonidine (Steingard et aI. 1993)
and Methylphenidate (Kratochvile et aI., 2002). Typically they are prescribed to target
self-regulatory processes including attention and response inhibition (Spencer et aI.,
1996). While medication effects can be quite positive for students with ADHD or conduct
disorder, a large number of students who struggle with self-regulation are not candidates
for pharmacotherapy.
Behavioral interventions for managing academic tasks have typically targeted
student behavior that supports self-regulated learning. Using observational techniques
28
such as functional behavior analysis, interventionists identify problem behaviors and their
precursors to identify mitigating factors (Sugai, Homer et aI., 2000). By altering
antecedents or reinforcing target behaviors and disciplining disruptive or non-
constructive behavior, behavioral interventions can reduce detrimental or increase
facilitating learning behavior (Sugai, Sprague, Homer, & Walker, 2000). While
behavioral approaches have been shown to be effective in preventing and shaping
behavior problems (e.g., Marquis et aI., 2000), they do not address the cognitive
capacities linked with self-regulated learning or specific academic skills.
The most promising line of research focusing on improving self-regulated
learning examines the impact of teaching cognitive strategies (Zimmerman, 1998). There
is increas~ng evidence that the processes supporting self-regulation in academic settings
are malleable (Graham & Harris, 1993; Margolis & McCabe, 2006). Student capacities
for self-regulation and self-reflection can be modified by direct instruction or teacher
modeling (Boekaerts & Como, 2005; Schunk, 1998; Zito et aI., 2007). Specifically,
research supports teaching contextually relevant metacognitive strategies to help students
attend to the 'cyclical process of self-regulated learning (Boekaerts & Cascallar, 2006).
There are several benefits ofthe cognitive strategy approach to helping students
self-regulate assignment management. First, interventions can be designed to target
specific difficulties in assignment management. For example, Hughes and colleagues
(2002) taught a group of students to manage their homework assignments independently
using a combination external organizer and metacognitive approach. Second, cognitive
strategies can be designed for applicability across a range of students. For instance,
29
Peterson and colleagues (2006) taught a group of students with diverse backgrounds the
same self-monitoring strategy. The results were improved behavior regulation in the
classroom across students. Third, the strategy complexity can be modified according to
student need and ability. The Hughes (2002) study described a complex homework
management system, while other studies evaluate simple monitoring systems such as
asking students to graph the number of problems completed during class (e.g.,
Shimabukuro et aI., 1999).
The most widely reported intervention approaches have been referred to as self-
management strategies (Belfiore & Hornyak, 1998). This review will address two general
self-management approaches: self-monitoring strategies and self-instructional strategies.
Also, a curricular approach to increasing academic self-regulation will be reviewed, as
will two examples of out-of-class assignment management strategies.
Self-Management Strategies
In their review of instructional strategies and self-regulated learning, Belfiore and
Hornyak (1998) outline an array of cognitive strategies that support "self-management"
in academic contexts. Self-management processes included self-monitoring, self-
instruction, informed decision making, problem solving, self-reinforcement, and self-
evaluation. The following review provides examples of strategies targeting self-
management that have been implemented with students who struggle with self-regulating
academic tasks.
30
Self-monitoring strategies.
Self-monitoring strategies have been taught to students with a wide range of
abilities functioning in diverse academic contexts. Self-monitoring procedures involve
student self-~valuations of whether the target behavior occurred or did not occur (Belfiore
& Hornyak, 1998). The evaluation is made either during or after task completion and the
student records the results for later review (e.g., Shimabukuro et aI., 1999). Data are often
recorded on task-specific checklists and the frequency of data recording varies according
to the frequency of the behavior (Belfiore & Hornyak, 1998).
Harris and colleagues (2005) reported the results of a self-monitoring intervention
for studying behavior in six students identified with ADHD. The target behavior was
defined as students independently gathering materials to study spelling words and
completing a short spelling exam while remaining on task throughout the procedure. The
strategy taught to students consisted of a self-questioning routine ("Was I paying
attention?") triggered by an audible tone played through headphones at varying intervals.
The studept would then record, "yes or no", whether he/she was paying attention on a
graph. The student also recorded the accuracy of their spelling test. The results revealed
positive effects for both remaining on task and improving spelling (Harris et aI., 2005).
Gureasko-Moore and colleagues (2007) taught six 11-12 year olds identified with
ADHD, a self-monitoring strategy to help manage "classroom preparation behavior".
They created a checklist defining four expected behaviors (e.g., student had pen or pencil
on desk) and asked students to indicate yes/no whether they accomplished the tasks. The
students checked in with thE researchers every day after class to review their self-
31
monitoring log. Following implementation of the self-monitoring procedure, the
percentage of classroom preparation behaviors increased across all six subjects as
measured by teacher report. The researchers then faded the checklist, instead using
student interviews to facilitate self-monitoring. Eventually, the researchers faded the
interviews, but student classroom preparation maintained a high level across students
(Gureasko-Moore, DuPaul, & White, 2007).
These are two examples of self-monitoring strategies applied to different contexts.
There are other studies that utilize ~ssentially the same procedures to target homework
completion (e.g., Shimabukuro et aI., 1999), improve math probl~m solving (e.g., Uberti
et aI., 2004), and increase academic productivity (e.g., Carr & Punzo, 1993) for students
with LD and ADHD, LD only, and behavioral disorders respectively.
Reid (1996) published a systematic review of intervention studies using self-
monitoring approaches for students with learning disabilities. The participants in the 23
studies more mostly between the ages of 9 and 11, although the range was 7 to 18 years
of age. In the research, which dated from 1979-1996, all but six studies were conducted
in a resource. or self-contained setting. Reid dichotomized the approaches as self-
monitoring of attention or self-monitoring of performance strategies. While no objective
metrics were provided (i.e., effect sizes), the author concluded that based on study results,
the effects of self-monitoring are most robust for on-task behavior and academic
accuracy. Also, the author stated, "(The effects) ... have been demonstrated across
differing age levels and instructional settings." (p. 318, Reid, 1996) There was no report
of generalization or maintenance of these effects in the review.
32
Se If-instruction strategies.
Self-instruction is a self-management strategy that utilizes sub-vocal recitation of
a series of steps to solve a problem (Belfiore & Hornyak, 1998). The use of self-
instruction as a teaching approach increases the likelihood that students will learn
independent self-management since the process lacks external mediation (Belfiore &
Hornyak, 1998).
Wood and colleagues (1993) published an example of a self-instructional strategy
targeting math problems for nine students between the ages of 8 and 11 identified with
learning disabilities. The strategy was taught in therapy rooms adjacent to the classroom
and utilized a teacher-recorded script played through headphones the students listened to
while working through the problem. For example, the first statement read, "First, I have
to point to the problem." The headphones were faded and the students were expected to
whisper or repeat the sequence sub-vocally. The results of the study indicated higher
percentage of problems attempted and completed accurately for all nine students (Wood
et aI., 1993). The results generalized to the faded headphone phase for six of nine
students. This example illustrates the typical self-instructional process. Self-instruction
may stand alone as a strategy, but is frequently incorporated in intervention packages
(e.g., Johnson et aI., 1997).
Self-instruction strategies are also useful for helping students manage their state
of mind whil.e engaged in academics. For example, Kamann and Wong (1993) published
a report examining the effects of teaching a self-talk strategy to students with LD on math
performance. The self-instruction strategy targeted feelings of anxiety and confusion
33
while working on problems. The students were taught to periodically complete a self-talk
routine including statements targeting assessment ("What do I have to do?"), recognizing
negative thoughts ("OK, I feel worried and scared."), controlling thoughts ("Take it step
by step."), and reinforcing ("Good for me.") while working on math problems in class.
The results of teaching students to assess their mental state revealed a statistically
significant increase in math problem productivity and accuracy for students identified
with LD (Kamann & Wong, 1993).
Syntheses ofselfmanagement research.
Two recent reviews of self-management interventions for students who qualify for
special education have been published. Reid and colleagues (2005) evaluated the effects
of four strategy instruction npproaches (self-monitoring, self-monitoring plus
reinforcement, self-reinforcement, and self-management) on academic behavior for
students identified with ADHD. Self-monitoring strategies included both self-monitoring
of attention and performance. Self-monitoring plus reinforcement includes adding
reinforcement from an external agent for change in target behavior (Reid et aI., 2005).
Self-reinforcement is the same approach as self-monitoring plus reinforcement but
students are taught to choose their own motivation. And self-management strategies, as
defined by Reid, are the same as self-monitoring except student self-evaluation of
accuracy is compared to teacher evaluations. Sixteen studies were included in the
analysis, half of which described self-monitoring plus reinforcement approaches, and four
studies were conducted in general education classrooms. The target behaviors across
studies included on-task behavior, inappropriate behavior, and academic accuracy and
34
productivity. Effect sizes were large across studies, especially for self-monitoring sample
with a range of 0.59 to 2.96. Self-monitoring strategies yielded the largest effect sizes for
reducing inappropriate behavior and increasing academic accuracy and productivity,
while self-management produced the largest effect size for on-task behavior. No
generalization or maintenance data were reported in this synthesis.
Mooney and colleagues (2005) published a similar synthesis of self-management
interventions for students identified with emotional behavioral disorders. They sampled
studies examining the effects of self-management instruction (self-monitoring, self-
evaluation, self-instruction, goal setting, and strategy instruction) on academic task
completion and accuracy. A total of 22 articles were included, published between 1970
and 2002, and the student age range was 5-21 years. Self-management strategies were
applied in math, reading, writing, social studies, and science. The majority of target
behaviors were related to academic production (e.g., duration of engagement) and
accuracy (e.g., % correct). Again, the effects sizes were very large ranging from -0.46 to
3.00. The mean combined effect size was 1.80. It should be noted that all the studies
utilized small sample sizes (n =7 was the largest single sample), and the effect of small
samples on effect size interpretation has been documented (Parker & Hagan-Burke,
2007). Generalization (two studies) and maintenance (13 studies) data were reported for
15 of the 22 studies, all reporting positive findings (Mooney et aI., 2005).
Self-Regulated Strategy Development
Self-Regulated Strategy Development (SRSD) is an intervention package
designed to teach metacognitive strategies explicitly (Graham & Harris, 1993). The
35
rationale for SRSD is adopted from social cognitive theory which supports the notion that
self-regulation skills are developmental and responsive to direct instruction (Zito et aI.,
2007). While the instructional sequence may be modified to teach strategies for any
academic task, the most common application of SRSD is for teaching writing strategies
(Graham &, Harris, 1993).
SRSD instruction follows a systematic approach that includes six general steps.
The following is a summary from Santangelo, Harris, and Graham (2008). Step one
involves instruction to develop student background knowledge of the essential skills
required for the task (e.g., components of narrative writing)~ Step two ("Discuss it")
focuses on building student motivation to learn a new strategy by.self-evaluation of their
own skills. Step three ("Model it") utilizes modeling as an instructional paradigm as the
instructor "thinks aloud" to demonstrate the strategy process (e.g., mnemonic to self-
evaluate complete story grammar). Step four involves helping students memorize the
strategy. Step five ("Support it") focuses on fading instructor supports, and step six
("Independent performance") emphasizes measuring strategy use, generalization, and
maintenance (Santangelo, Harris, & Graham, 2007). The SRSD paradigm has been
evaluated in a number of writing experiments (see review in Zito et aI., 2007), and
experimental results indicate positive effects for both writing knowledge and improved
in-class process management (e.g., Graham et aI., 2005).
Out-o.fClass Assignment Management Strategies
A number of reports have been published examining the effect of strategy
instruction on out-of-class assignment management. One example of an out-of-class
36
assignment strategy was published by Olympia and colleagues (1994). The researchers
trained the classroom teacher to implement a homework completion strategy for 16
students who struggled with math. Following two training sessions, teachers taught
students to graph the number of assignments they completed and their percentage
accuracy. The results indicated increased pre-post math achievement scores and increased
curriculum-based assessment scores for the group. Homework completion and accuracy
results were mixed, as measured by visual analysis of single subject research design data,
due to high variability of target behaviors (Olympia, Jenson, Sheridan, & Andrews,
1994).
A widely cited example on a homework completion strategy is provided by
Hughes and colleagues (2002). The researchers taught students with learning disabilities
an assignment management strategy that incorporated several metacognitive and memory
components. Students were taught to record new assignments in weekly and monthly
schedules. They also taught students a self-regulation strategy to actively manage the
assignment completion process. The acronym PROJECT helped students remember the
process. Briefly, PROJECT stands for Prepare, Record, Organize, Jump to it, Engage,
Check, Turn in. The Organize step was further broken down into four steps, and the
mnemonic BEST help students self-monitor this phase: Break into parts, Estimate time,
Schedule, Take materials home. The strategy was modeled and reinforced by the
researchers during 30 minute instructional sessions held four days a week for four weeks.
Of the nine s~udents in the study, eight demonstrated mastery of the strategy for both
simulated assignments and work assigned in general education classrooms. As a group,
37
the students completed more assignments, increased GPA over the course of the year, and
were rated higher by teachers on measures of assignment management and completion
(Hughes, Ruhl, Schumaker, & Deshler, 2002).
Gaps in Intervention Literature
W.hile the number of research studies describing effective self-regulated learning
strategies for students who struggle academically is increasing, there are several gaps in
the literature base. One component of self-regulated learning intervention is
implementation. In a study attempting to evaluate the PROJECT homework intervention
(Hughes et aI., 2002), the current researcher encountered significant and unexpected
implementation barriers in when working in a typical special education context. Barriers
included student resistance due to feelings of alienation when they were pulled out of
class, erratic student attendance, and inconsistent strategy reinforcement across teachers
(J~ess & Sohlberg, 2007). The results of these barriers were poor treatment delivery and
adherence.
A follow-up pilot study evaluated a modified assignment completion process
strategy designed to mitigate the previously identified implementation barriers.
Modifications included simplifying the materials, training the classroom teacher as the
primary strategy mediator, and incorporating the strategy within the classroom context.
These modifications proved successful, and the strategy resulted in improved in-class
assignment management skills for students in a resource room context ~ess, Sohlberg, &
Albin, in preparation).
---------------_.-
38
In addition to implementation barriers, another gap in strategy instruction
literature is examining the broader impact of improved academic task management. The
majority of self-management strategy research focuses on academic productivity and
accuracy of specific tasks (e.g., Reid, 1996). Evaluation of other possible outcomes of
self-management strategy use such as improved independence in assignment completion
or changes in self-efficacy have been neglected. For instance, little is known of the
effects of improved assignment management on motivational vari'ables such as academic
self-efficacy for students who qualify for special education. Further, it has not been
established whether students who learn a self-regulation strategy for one task will
generalize strategy use for other academic tasks. The current study was designed to
further the cognitive self-regulation strategy research and address some of the current
research gaps.
Research Questions
The research questions guiding this investigation were:
Student Implementation
1. Can students with diverse academic needs employ a self-regulated assignment attack
strategy (SAAS) when taught as a whole group in a resource setting as measured by their
ability to verbalize and demonstrate key elements of the strategy?
Teacher Implementation
2. Can teachers implement and maintain the critical elements of the SAAS with high
fidelity as measured by direct observation and teacher report following two thirty-minute
training sessions?
39
Social Validity
3. Is SAAS perceived as a practical and effective strategy as measured by a modified
version of the Treatment Evaluation Inventory-Short Form (teach~r) and modified
Children's Intervention Rating Profile (student)?
Assignment Attack
4. What are the effects on assignment attack for students who learn and apply SAAS
during study supports class?
Generalized Impact
5. What are the general academic effects for students who apply SAAS as measured by
quarterly gra.de reports and classroom grades and assignment completion rates in math
and social studies?
6. Do the students who implement SAAS report higher general academic strategy use,
motivation, and academic self-efficacy as measured by self-report?
7. If students improve independent assignment management, do the benefits generalize to
activities requiring self-regulation in non-academic settings as measured by the Behavior
Rating Inventory of Executive Functions (BRIEF: parent version)?
8. What are the behavioral effects for students who learn and apply SAAS as measured
by the Home'work Behavior Checklist (HBC) and on-task behavior in class?
40
CHAPTER III
METHODOLOGY
This section describes the research activities completed during this study
evaluating the effects of a self-regulated assignment management intervention package
on in-class assignment attack behavior using a single subject multiple-baseline design.
The target population for this study was students who qualify for special education and
struggle to manage the assignment productivity requirements inherit in secondary
education settings. In addition to in-class assignment management behavior, this study
examined the generalized impact of self-management strategy instruction on different
domains of self-regulated learning including improvements in academic strategy use and
cognitive-be~aviorself-regulation processes.
This chapter describes the methodology applied for the study. First, a summary of
the pilot study preceding this project is provided, followed by a description of how the
current work was designed to extend the pilot findings. Next, a description of participant
characteristics and recruitment is provided. The research design elements and data
analysis procedures are subsequently detailed.
Pilot Study
The pilot study (Ness, Sohlberg & Albin, in preparation) evaluated an
intervention designed to improve student management of assignments. Pre-pilot research,
41
based on the Hughes and colleagues' (2002) PROJECT model, had revealed
implementation barriers for strategy instruction inherent in "real-world" special education
settings (Ness & Sohlberg, 2007). The pilot study attempted to address these
implementation barriers by using whole-class instruction and targeting teacher
implementation of strategy instruction. The pilot study evaluated the effectiveness of a
classroom-based external assignment tracking system on individual student assignment
attack behavior.
Pilot Methods
Six students in a resource context, all of whom qualified for resource support
based on academic difficulty and failure to manage required assignments, participated in
the intervention. All six students in the classroom participated in this study because the
intervention was designed to be part of the daily instructional routine. Outcome data were
collected on the three students with the lowest assignment attack scores.
An interview with the special education teacher was conducted to identify a
strategy that would work with his classroom routines and the school scheduling. An
external assignment management tool was developed consisting of four components: (l)
a bulletin board, (2) assignment recording tickets, (3) student access to bulletin board to
remember assignments, and (4) reinforcement for completing assignments. A bulletin
board was installed in the back of the classroom and was divided ~nto seven columns (one
for new assignments, six for students) with heavy black lines. After a demonstration by
the researcher, the teacher taught students to record target class, assignment detail, and
due date on the "assignment tickets". He selected student volunteers to complete tickets
42
for new assignments daily and post them on the bulletin board. The teacher was
instructed to refer students to the board to remind them what outstanding assignments
they needed to complete. The teacher instituted a reinforcement consisting of a weekly
raffle using completed assignment tickets where students could win rewards (e.g., "Front
of the Line passes").
The dependent variable consisted of an assignment attack protocol developed and
validated for. this study. Assignment attack comprised four student behaviors requisite for
independent assignment management: (1) recalling assignment details, (2) gathering
necessary materials, (3) initiating seatwork, and (4) ongoing task engagement. The
assignment attack behavior was operationalized and high inter-rater reliability was
established following multiple classroom observations and teacher interviews prior to the
initiation of the pilot study. Assignment attack data were collected through classroom
observation using the measure depicted in Figure 1.
The pilot study featured an ABAB withdrawal design (Kennedy, 2005) to
evaluate whether assignment attack behavior changed as a function of having the
classroom assignment management strategy with the bulletin board and teacher supported
process as detailed.
Pilot Results and Conclusions
Results indicated the assignment management strategy and assignment attack
behavior were functionally related for two of the three students. Large and immediate
changes in assignment attack level were demonstrated. The third student's assignment
attack behavior improved, but high variability limited the interpretation of the findings. A
Figure 1
Assignment Attack Rating Scale.
Student behavior Score
(1-5)
1. Student recalls homework assignment during homework support period.
Recall requires knowing academic subject and assignment details.
I = Teacher must tell student assignment or show student pink sheet/folder. Student has no recollection of assignment and acts like they I
have no work.
2 = Teacher tells student assignment or shows student pink sheet/folder. Student acts like they know what to do but doesn't know subject 2
and details.
3 = The student knows about assignment if asked or checks pink sheet/planner with teacher once, must get repeated teacher clarification 3
regarding the details of the assignment (i.e., teacher pointing to pink sheet/folder or verbal instructions on multiple occasions).
4 = The student knows both the subject (e.g. science) and the details of the assignment and demonstrates this by gathering materials, but may 4
ask one clarifying question of teacher. Teacher does not need to review pink sheet or folder with student.
5 = Student requires no support for knowing subject and assignment details. 5
2. Student gathers appropriate material for assignment.
Gathering materials requires looking for and retrieving book/worksheet, pencil/pen, and related materials.
I = Teacher gives materials without independent student attempt to open bag, get notebook, etc. I
2 = Teacher tells student materials and helps student retrieve them. 2
3 = Teacher tells student what materials and student retrieves them. Usually prompts to get material trom locker. 3
4 = Teacher must clarify materials need after student retrieves some materials. 4
5 = Student independently looks for and retrieves materials or independently asking to leave and retrieve materials from another location. 5
3. Student initiates assignment completion.
Initiating assignment refers to making a first attempt at working.
I = Teacher must visit student at desk more than once to tell student to begin working on new assignment. I
2 = Teacher either visits student once or has to provide multiple verbal prompts before student starts on new assignment. 2
3 = Teacher provides only one verbal command to start working. 3
4 = Teacher only makes indirect comment like "it's time to work on your assignments" to prompt student to begin work. 4
5 = Student begins work with no individually directed teacher prompts. 5
.,J:::.
w
Figure 1 (Cant.)
4. Whole-class task persistence.
Engagement means actively working on assignment towards completion.
I = Student looks around class or turns head from work once every minute or two, and/or wanders from desk more than once. Repeated I
teacher prompts to keep working.
2 = Student looks around class/turns head every five minutes or so. May wanders from desk once. Repeated teacher prompts to keep 2
working.
3 = Student looks around class and turns head once every five minutes or so. Doesn't wander from desk. Teacher provides one or two 3
prompts to remain on task.
4 = Student may look up and look around once every five minutes. 4
5 = Student remains on task for the entire period, requires no teacher prompts to remain engaged. 5
On-task Behavior:
1 minute 2 minutes 3 minutes 4 minutes 5 minutes
6 minutes 7 minutes 8 minutes 9 minutes 10 minutes
~
~
45
maintenance check conducted one month after study completion revealed that the teacher
had continued use of the strategy and students' assignment attack behavior maintained
previous high levels.
The contribution of this study was threefold. First, the results supported findings
in the self-regulation literature that strategy instruction improves academic performance
for struggling students. Second, the successful implementation suggested that teaching
strategies at the level of the classroom and utilizing the classroom teacher as primary
implementer is effective in a special education setting. Third, this study provided a
validated measure of in-class assignment management behavior.
Dissertation
The current study extended the previous work by incorporating the metacognitive
or self-regulation aspect of strategy training that appears to be key in long-term
maintenance and generalization. The pilot study relied on external supports that were not
faded. Improvements in student behavior were the result of more organized, purposeful
behavior (i.e., facilitated executive functioning), but the metacognitive capacity for self-
regulation was not addressed. Students were not taught to construct an internal schema
for self-regulating their assignment attack. The current study addressed this limitation by
scaffolding a metacognitive approach to assignment attack using external supports, and
systematically fading the supports to facilitate self-regulation.
Participants·
The participants (N = 3) for this study were selected based on the following
inclusion criteria: (1) currently enrolled in sixth grade; (2) qualify for special education
46
services; (3) enrolled in general education classes, but spend a portion of their day in a
resource room for help managing out-of-class assignments; (4) low academic
achievement as measured by classroom grades; and (5) dependent upon teacher for
organizing materials and initiating their work in the resource context as reported by
teacher and confirmed by researcher observation using the assignment attack protocol.
Recruitment and consent.
University of Oregon IRB approval was ongoing from the pilot study and the
Bethel School district agreed to facilitate the project. A modification to the original
university IRB application was approved allowing alterations to intervention and research
design as well as collecting additional outcome data for this study. Through word of
mouth from the teacher participating in the pilot study, a sixth grade special education
middle school teacher at a different school expressed interest in the study and provided
written consent to apply the strategy during her homework support class and to complete
measures.
All students in the class were invited to participate in the study. The teacher sent
home recruitment and consent letters to parents/caregivers. The intervention was applied
as a part of the classroom routine so all students learned the strategy. However, data were
collected only on students whose parents returned signed consent. Three students were
selected for data analysis based on teacher nomination and low baseline assignment
attack behavior.
47
Setting
In the host middle school, there were three different resource support classes for
sixth graders. The special education teacher taught the second period class, and she
supervised educational assistants teach two separate homework support classes during
third period. The participating teacher had six years of teaching experience, all in special
education and in the same middle school. Over the duration of this study, there was one
educational assistant for one third period class and three different assistants for the other.
Each study support class was 45 minutes in duration and was designed to provide
struggling students support for managing assignments and academic responsibilities in an
inclusive setting. There were approximately twelve students in the second period group
(including Adam), and there were six students and three students in each third period
group (including Brett and Christina respectively). One participating student was
nominated and selected from each group. The demographic information about these
students is presented in Table 1.
Adam
Adam was a twelve-year-old boy who qualified for special education based on
identifications of specific learning disability and speech-language impairment. Adam's
struggle with assignment attack included rarely bringing materials to class and difficulty
remembering outstanding a~signments, according to the special education teacher. His
success during assignment completion time seemed largely dependent on internal factors
(i.e., mood when class started) independent of work expectations. His academic supports
and accommodations included a behavioral support plan, reduction of large homework
48
assignments, preferential seating, extended time for reading and writing assignments,
allowing healthy snacks during class, morning check-in, teacher check for
comprehension, and increasing active participation.
Table 1
Demographic Data
Student
Adam
Age Gender Ethnicity
12 M lW/C
Free/Reduced
Lunch
Yes
IDEA
Identification Full Scale IQ
Brett
Christina
12
12
M
F
W/C
W/C
No
No SLD
86
(WISC-IV)
108
(WAS!)
I White/Caucasian
2 Specific Learning Disability
3 •
Speech or Language Impairment
4 Emotional Disturbance
5 Reynolds Intellectual Assessment Scale
Brett
Brett was a twelve-year-old boy who qualified for special education based on an
identification of emotional-behavior disturbance. He was selected to participate in this
study due to struggles initiating assignment attack, most often the result of refusing to
start non-preferred assignments. According to the special education teacher, Brett was
able to stay on-task once he started, but even with preferred assignments, teacher support
was necessary to assemble materials. Brett's other academic supports and
49
accommodations included providing immediate teacher feedback, repeating directions,
preferential seating, re-teaching expected behaviors, and teaching self-monitoring. The
primary manifestation of his disability was defiance toward adult instructions or anger
following requests to change activities.
Christina
Christina was a 12-year-old girl who received study supports due to an
identification of specific learning disability. Her learning disability was secondary to a
diagnosis of phenylketonuria (PKU), which is an inherited metabolic disorder associated
with impaired conversion of the amino acid phenylalanine. Excessive phenylalanine can
result in damage to the central nervous system and subsequent developmental disabilities
(National Institute of Child Health and Human Development, 2009). Christina's
assignment attack difficulties were related to poor initiation and sustained attention as
well as problems remembering assignment details according to the special education
teacher. Other academic supports and accommodations included teacher check for
comprehension, breaking down length assignments/projects, reducing workload,
providing oral directions, sitting in close proximity to the teacher, teacher support with
homework plamler, and extended time for assignments.
Research Design
The approach implemented to address the primary research questions was a
multiple baseline across participant design. This design was selected to evaluate the
functional relation between the Self-regulated Assignment Attack Strategy (SAAS)
described in the next section and changes in student behavior. Unlike traditional,
50
between-subject group designs involving random selection, single subject methodology
facilitates the evaluation of functional changes in individuals over time. Single subject
methodology is particularly useful in intervention studies because each participant serves
as his/her own control. This reduces threats to internal validity, such as maturation,
common in group designs (Kennedy, 2005). It is particularly useful when the population
is heterogeneous and the research questions seek to find clinical or practical significance
of changes (Homer et aI., 2005).
In contrast to statistical analyses in group designs, experimental control is
evaluated.using visual analysis of raw data in single subject designs (Franklin, Gorman,
Beasley, & Allison, 1997). Changes in behavior between baseline and intervention phases
(i.e., change in level, trend, and/or variability) indicate a functional relation between the
intervention and target behavior (Homer et aI., 2005; Kennedy, 2005). Three clear
instances of behavior change at three different points of time across participants in
multiple baseline designs has been recommended as the guideline for demonstrating
experimental control (Homer et aI., 2005; Parker & Hagan-Burke, 2007)
In this study, the baseline condition represented normal classroom conditions. The
intervention condition represented implementation of SAAS. To facilitate self-regulation,
the external prompts were faded in the intervention phase. This design permitted
evaluation of whether SAAS and assignment attack were functionally related for students
in the resource room. Also, this design enabled evaluation of whether the effects were
externally mediated or if students learned to self-regulate assignment attack.
51
Because other constructs under investigation, such as self-efficacy and volitional
academic .strategy use, were not expected to change on a daily basis, these domains were
evaluated using pre- and post-intervention measurement.
Intervention Package: Self-Regulated Assignment Attack Strategy (SAAS)
The independent variable consisted of an assignment completion strategy
package, the Self-regulated Assignment Attack Strategy (SAAS), containing a teacher-
supported checklist with key assignment management behaviors that the students could
follow in a classroom setting. Teacher support was faded over time with instruction to
have students internalize the process by "saying the steps in their heads". All instruction
was scripted promoting consistent implementation across groups (see Appendices 2 and
3).
There were three critical components of SAAS. First, a step-by-step student
checklist was presented, scaffolding self-regulation behaviors for students, as shown to
be important in the self-management literature (e.g., Reid et aI., 2005). Another important
facet of the package was systematic instruction and teacher demo~stration.The scripts
and processes were fashioned after the SRSD model (Graham & Harris, 1993). Finally,
the fading of external support to internalized self-regulation is necessary for long-term
maintenance as discussed in the self-regulation literature (e.g., Boekaerts & Corno, 2005;
Zimmerman, 1998)
Checklist: The Four P's.
The external support for self-regulating assignment attack was a checklist given to
students during the resource period (see Figure 2). The four steps were: (1) Planner/Pink
52
Figure 2
The Four P 's Checklist.
The Four P's
How Did I Do?
IMon8wed~
-
-
Proceed
Planner Check I D CD
~ick an Assignment I D IT
~repare Materials I D IT
I D IT
Key:
~ = Did not have to be told
- = Had to be told
53
sheet check, (2) Pick an assignment, (3) Prepare materials, and (4) Proceed. External
supports ~re effective for modeling self-regulated learning processes, such as self-
monitoring (e.g., Harris et ai., 2005). We demonstrated the effectiveness of using external
aids to support assignment attack behavior for students in a resource setting in the pilot
study.
Systematic instruction.
The Self-Regulated Strategy Development (SRSD; Harris & Graham, 1993)
instructional model has been replicated in numerous studies teaching students who
qualify for special education to self-regulate academic tasks (e.g., Graham et aI., 2005;
Zito et aI.; 2007). The SRSD model includes six steps the teacher performs sequentially.
The six steps are illustrated in Figure 3. The initial instructional sequence for introducing
SAAS was mapped onto the SRSD Phases 1-3. The instructional sequence for fading the
external supports mapped onto SRSD Phases 4-6.
The instruction was scripted to promote consistent explanation across groups and
maximize effective implementation. Three instructional goals were explicitly addressed
in the script. First, the teacher was prompted to teach the strategy using direct instruction
principles known to facilitate cognitive strategy acquisition for students who struggle
academically (Kameenui & Simmons, 1990). Specifically, the teacher was prompted to
explain the four steps of the checklist, model the procedure, and assess students'
understanding through questions. Second, student motivation to apply the strategy in
class was targeted in the script. The teacher was prompted to lead students in a discussion
54
about their experiences completing assignments and their need to manage this academic
skill more independently in the future. Establishing personal relevance is a powerful
predictor in student acceptance and use of self-regulation strategies in classrooms
(Schunk, 1998). Third, the teacher was prompted to model self-regulation processes using
Figure 3
Self-Regulated Strategy Development Stages*.
Stage 1: Develop Background Knowledge
Help students develop understanding of essential skills and knowledge of academic
content, strategy, or task.
Stage 2: Discuss It
Teacher and students discuss current academic performance and need for effective
strategies to support learning. The teacher introduces the proposed strategy and explains
the purpose ~nd benefits.
Stage 3: Model It
The teacher models how to use the strategy using examples and "talk-aloud" procedures,
explaining each phase of the strategy as it is completed.
Stage 4: Memorize It
This stage is not necessary for all students, but many students who qualify for special
education benefit from memorization practices. To help students memorize elements of
learning strategies, mnemonics can be applied to critical features.
Stage 5: Support It
Teacher and students use the strategy collaboratively during academic tasks.
Stage 6: Independent Performance
During the final stage, students use strategies and self-regulation processes
independently. Students are encouraged to say steps or procedures covertly "in their
heads" to fade the use of external supports.
*Adapted from Graham and colleagues, 1998.
55
self-appraisal statements during strategy demonstration. The goal.of SAAS was to
increase students' evaluation of their cognitive processes during assignment completion,
and teacher modeling of self-appraisal statements is an effective method to convey self-
regulation of learning (Paris & Winograd, 2001).
Faded external supports.
The components of SAAS were intended to facilitate internalization of self-
regulatory processes. First, SAAS uses a mnemonic to help students remember key
behaviors. Mnemonics are powerful memory strategies that work by producing mental
representations of information that facilitate ease of recall. A classic example of a
mnemonic in the form of a poem is, "When two vowels go walking, the first one does the
talking." SAAS uses a first letter mnemonic strategy to help students remember key
assignment management behaviors. All four behaviors begin with the letter "p", so the
self-regulated assignment management process was referred to as the "4 P's".
Second, the "4 P's checklist" was systematically modified (see Figure 4) and
faded using the method of vanishing cues (Baddeley, 2004) until students were instructed
to say the four steps "in your head" without the support of a checklist. Fading external
supports using self-instruction, or "self-talk", is an effective approach for helping
struggling students develop self-regulated learning practices (e.g., Johnson et aI., 1997).
Current homework strategy.
The intervention package was designed to be flexible so that it could work in
variety of classroom/school contexts with differing teacher practices. The key
components including the checklist of self-regulation behaviors, teacher modeling and
56
scripts, and fading of external prompts could be adapted to a variety of school and
classroom routines. In this study, SAAS was superimposed on the existing homework
management system described below.
The students in the target sixth grade resource room used a daily planner intended
to help them remember assignments from general education classrooms. They were
expected to use the school's daily planner to remember their assignments. The planner
consisted of daily sheets with pre-printed rows delineating the seven periods of the school
day. The students were instructed to write new homework on the planner the moment
assignments were assigned. The students were then prompted by the teacher to check
their planner during resource period and work on outstanding assignments. Verbal report
from the special education teacher indicated these students had difficulty entering their
assignn1ents consistently and accurately necessitating the creation of a second system -- a
weekly log of unfinished assignments. The student carried this "pink sheet" to each of
their teachers once a week and the teachers wrote unfinished assignments on the sheet.
The special education teacher then prompted the students to take the sheet home and have
their parents/caregiver sign it. The pink sheets were used in the resource room to help
students prioritize assignments.
Discussions with the special education teacher indicated that she wanted to
maintain use ofthe school planner and "pink sheet" but desired to help students improve
their indepe~dent assignment completion skills as the current system was not meeting this
goal.
Figure 4
Modified Checklist.
p
-
p
-
p
-
p
-
57
The Four P's
How Did I Do?
IMonEJwed~
I D []]
Key:.
.y = Did not have to be told
- = Had to be told
58
Dependent Variables
The research questions guiding this study sought to answer questions about the
potential impact of an academic strategy instruction package on both academic outcomes
and broader cognitive-behavioral domains. Therefore, the dependent variables for the
study were organized into those that measured academic outcome and those that
measured self-regulated learning variables.
Academic outcome.
Five measures were administered to evaluate the impact of SAAS on academic
outcome: (1) assignment attack, (2) academic achievement, (3) assignment completion
behavior, (4) assignment completion rate, and (5) proportion of time spent on-task.
Assignment attack was measured using the scale illustrated in Figure 1. The
measure sampled four domains of independent assignment management: (1) recalling
assignments,. (2) gathering materials, (3) initiation, and (4) whole-class engagement. The
face and content validity of the scale were confirmed by implementation in a special
education resource context (Ness, Sohlberg, & Albin, in preparation). The assignment
attack sco.re is a composite offour domain scores (recall, materials, initiation, and
engagement); each domain was measured on a 1-5 scale depending on the level of teacher
prompting necessary for each area. The minimum score was four and the maximum score
was twenty. Daily assignment attack data comprised the dependent variable in the pilot
study. Reliability checks were conducted during the pilot study and researcher scores
were highly correlated (r = .90) indicating this measure yielded consistent scores across
raters.
59
Academic achievement was measured using changes in social studies and math
grades over the course of the term. The grades were calculated on a percentage basis
(100% maximum). The special education teacher endorsed these two classes for outcome
measurement since students were assigned homework frequently and the data were
accessible using the existing progress monitoring program at the school.
Assignment completion behavior was measured using teacher and parent reported
perceptions of student behavior during academic tasks. The measure was the Homework
Problem Checklist (HPC, Anesko, Schoiock, Ramirez, & Levine, 1987). The HPC,
illustrated in Figure 5, has been validated to measure out-of-class assignment completion
behaviors by students who qualify for special education (e.g., Epstein et aI., 1993). The
questionnaire consists of20 items, and uses a 0-3 Likert-type rating scale. While the scale
has not been normed, previous studies have demonstrated the internal consistency
(Cronbach alpha = .91) and discriminative validity of the HPC (Anesko et aI., 1987).
Assignment completion rate was measured using the school progress report
system. The host middle school produced regular progress reports for students. Included
on these reports was a list of completed and unfinished assignments. Assignment
completion rate was calculated by dividing the number of completed assignments by the
number of total assignments in a one-month period. A one-month)nterval was selected
because this was a reasonable amount of time to gather a representative sample of
assignment completion according to the special education teacher.
On-task behavior during assignment completion time was defined as the student
actively engaging in work or seeking help after initiating academic tasks and the absence
60
of off-task behavior (Amato-Zech, Hoff, & Doepke, 2006). Off-task behavior was
operationalized according to the framework described in Amato-Zech and colleagues
(2006): off-task motor, off-task verbal, or off-task passive. Off-task motor included any
Figure 5
Homework Problem Checklist.
For each statement, rate the frequency of each problem's occun'ence in the past two
weeks using the 1011O\ving scale: never (0); at times (1 ); often (2), and very often (3).
"'--~--"-""_'_-"------~-~---~----~..--,----------,
Fails to bring home assignment and necessary materials I
(t~~!2Q9-~~_~Q~~~}1~t~!_~c.1. +.U_~ l_._.__.._~_
Doeso't know c~ac.~.what hCf~~e.wQrk.has been ass,i~cd':'- t-'0_1_2_3 -1
Denies 'having homework assignment 0 1 2 3
Refuses to do homework asshmment. °1 2 3
~~Vhin~rcornPtl!!!!~abouth211leworL~ 0 1 2 3
Must be reminded to sit down and start homework. 0 1 2 3
Procrastinates, puts offdoing homework. 0 J 2 3
Doesn't do homework satisfactory unless someone is in the 01 2 3
mom. I
D?c~'n~t ~oh()mework satisfactorily unless someone docs it i0 ] 2 3
wIth hmliher. ~~--------1
Daydreams or plays. with object\) during homework session. °1 2 3
Easily distracted by noises or activities ofothers. 0 1 2 3
o 1 2 3
o 1 2 3
Deliberately fails to bring assignment back to class,
!S~lP.Q.f-l~~Q25!r_~~~~~I!J2!2_~},J~,~r~~.~~J2~21}"~~t,,~2!!!~~9!~· t o,,,,,,,,1__",w,2,,,,, ".3"',, -"'~"-''''-''-''''''-'1
Produces messv or sloppy homework. 0 1 2 3
Hurries through homework and makes ~3!!~Te§s !nist~k~;.~~,--·~"i'O'·""'1"·-'-2·'~-3,,~~,,_w'-~'--l
Shows dissatisfaction with work, even when he/she docs a 0 1 2 :3
good job.
'--~---"-'---------------+----~-----l
forgets to bring assignment back to class.
61
motoric movement not associated with the task including randomly flipping pages, out of
seat, etc. Off-task verbal included any audible verbalization not associated with the task
or permitted during the task such as talking to peers, humming, calling out answers. Off-
task passive included passive disengagement for a period of at least three consecutive
seconds.
Cognitive-behavioral variables.
To address the questions of the potential impact of SAAS on broader cognitive-
behavioral factors, this study included non-assignment completion related variables.
Changes in executive functions, academic self-efficacy, motivation, academic strategy
use, and actiJ!g-out behavior were measured as potential indicators of domain-general
cognitive-behavioral changes.
Generalized changes in self-regulated cognitive processes were assessed using the
Behavior Rating Inventory of Executive Functions: Parent Form (BRIEF, Gioia, Isquith,
Guy, & Kenworthy, 2000b). The BRIEF is an 86 item questionnaire designed to capture
behavioral correlates of impaired executive functions in everyday situations (e.g., getting
ready to go to school). The measure yields three standardized scores: the Behavior
Regulation Index, the Metacognitive Index, and the General Executive Composite. The
Behavior Regulation Index includes items that sample executive domains including
inhibition, set shifting, and emotional control; the Metacognitive Index consists of items
measuring initiation, working memory, planning/organizing, organization of materials,
and self-IlJonitoring. The General Executive Composite is a sum of the two indices. The
index and general raw scores are standardized and reported as T scores with percentile
62
ranking, and higher scores represent greater perceived dysexecutive symptoms (Gioia et
aI., 2000b).
The original normative sample for the BRIEF did not include students who
qualified for special education, but a subsequent study demonstrated the validity of the
scale for evaluating executive function deficits for students identified with ADHD,
autism, traumatic brain injury, and learning disability (Gioia, Isquith, Kenworthy, &
Barton, 2002). During initial evaluation of the scale, the test-retest reliability coefficient
of the BRIEF-parent form was .81 for a duration of two weeks, internal consistency was
.80-.98, and inter-rater reliability between teachers and parents was .32 (Gioia, Isquith,
Guy, & Kenworthy, 2000a). The low inter-rater reliability was considered a function of
evaluating students in different contexts where presumably the behavioral impacts of
executive function impairment were context dependent. There were no inter-rater
comparisons of BRIEF scores in this study since only the parent form was administered.
To examine the potential impact of SAAS on broad constructs of self-regulated
learning, a scale was created sampling three student characteristics: academic self-
efficacy, motivation, and academic strategy use (Figure 6). The 13 items on the scale
were selected from existing measures, but the content was significantly altered to match
the purpose of this study. The original items were selected from the Academic Volitional
Strategy Inventory (McCann & Turner, 2004), the Self Efficacy of Learning Form:
Abridged (Zimmerman & Kitsantas, 2007), and the Motivation section of the Motivated
Strategies for Learning Questionnaire (Pintrich et aI., 1991). The question wording was
modified for developmental appropriateness and relevance to middle school academic
63
Figure 6
Student Se(fregulation Probe.
Please circle the number that best fits you.
Always Sometimes Never
1 2 3 4 5
1. I promise myself something like watching a movie if! study. (8) 1 2 3 4 5
2. I tell myself, "you can do this!" when if! get stuck on 1 2 3 4 5
assignments. (8E)
3. On difficult assignments, I remember how I did a similar work in 1 2 3 4 5
the past. (8)
4. I prefer difficult assignments because I like work that challenges 1 2 3 4 5
me. (M)
5. When I don't feel like working, I remember how important it is 1 2 3 4 5
to graduate. (8E)
6. I ask for help when I get stuck on assignments. (8) 1 2 3 4 5
7. I really like to get good grades on assignments. (M) 1 2 3 4 5
8. When I try hard, I learn from all of my assignments. (M) 1 2 3 4 5
9. I remind myself to stay focused if! get distracted. (8) 1 2 3 4 5
10. I like to find new ways to make assignments interesting. (8E) 1 2 3 4 5
11. I am good at getting caught up on assignments. (8E) 1 2 3 4 5
64
Figure 6 (cont.)
12. I feel I let myself down if I don't keep up with school work. (M)
13. If I try, I can get the best grade in class. (M)
SE = Academic self-efficacy. S = Strategy use. M = Motivation.
1
1
2
2
3
3
4
4
5
5
contexts. This was an informal measure since it had not been validated or normed for this
population. It did, however, have high face validity and theoretical grounding being a
composite of questions from existing measures.
Treatment fidelity.
Treatment fidelity refers to the accuracy and consistency with which independent
variables are delivered (Gresham, MacMillan, Beebe-Frankenberger, & Bocian, 2000).
Documenting treatment fidelity is essential for demonstrating a strong relationship
between the treatment and the outcome variable, as well as supporting the external
validity of research findings (Gresham et aI., 2000). Because this intervention was
conducted in a school setting and not a laboratory context, it was important to measure
the implementation. Adequate treatment fidelity is critical to the validity of inferences
that can be drawn about the impact of the treatment (Zvoch, Leternou, Parker, 2007).
Following Gersten and colleagues' recommendations (2005), both fidelity of
intervention delivery ("treatment delivery") and fidelity of student implementation
("treatment adherence") were measured (See Figure 7). Treatment delivery and
adherence fidelity data were measured once a week during each phase. A total percentage
65
Figure 7
Treatment Fidelity Worksheet.
Treatment Delivery Intervention Phase
1. The teacher makes a clear declaration that assignment completion time begins.
2. Teacher hands out 4 P's checklist.
3. Teacher asks students to fill out "How did I do?" boxes.
4. Teacher reads at least one self-reflection statement during self-evaluation.
5. Teacher collects students' checklist after each class.
First two days only:
1. Teacher explains need and benefit of using strategy.
2. Teacher models use.
3. Teacher asks students reflection questions.
Treatment Delivery Fading
Initial sequence (two weeks)
1. The teacher makes a clear declaration that assignment completron time begins.
2. Teacher hands out the 4 P's checklist.
3. Teacher asks students to fill out "How did I do?" boxes.
4. Teachers initial each students' checklist after each class.
+/-
Fade procedure
1. The teacher makes a clear declaration that assignment completion time begins. §
2. The teacher hands out shortened checklist (first two weeks only)
3. The teacher asks students to say the 4 P's "in your head".
Treatment Adherence: Student use of SAAS
1. Students recite the four P's when prompted (esp. first week Phase B).
2. Students look at the template at least once while working on assignments.
3. Students record checks or minuses on the checklist.
4. Students report reciting 4 P's "in your head". (first week Phase B')
66
was calculated for both treatment delivery and treatment adheren~e. The outcome
variable for both was total percentage (Gresham, 1997).
Social validity.
Social validity has been defined as "the estimation of the importance,
effectiveness, appropriateness, and/or satisfaction various people experience in relation to
a particular intervention" (Kennedy, 2005, p. 219). Social validity is an important
construct since research consumers are less likely to implement interventions they
perceive as less acceptable (Foster & Mash, 1999). Perception of SAAS as a socially
valid intervention was measured to examine relationships between fidelity, student
outcomes, and replication likelihood.
T~e most common measure of social validity is participant questionnaire
(Kennedy, 2005). For this study, teacher and student perception of treatment acceptability
was measured using questionnaires. The teacher questionnaire contained four items based
on questions from the Treatment Evaluation Inventory-Short Form (Kelly et aI., 1989).
The teacher rated each item using a five level scale. Also, a fifth item was included; an
open ended question asking for teacher input for making the intervention more
acceptable. Student perception of intervention acceptability was measured using a
modified version of the Children Intervention Rating Profile (CIRP; Turco & Elliot,
1986). See Figures 8 and 9 for illustrations of these measures.
67
Figure 8
Social Validity Measure for the Teacher.
Place an 'X' in the corresponding box below:
Strongly Disagree Neutral Agree Strongly
Disagree Agree
I think the students liked the
assignment completion
strategy (i.e., the 4 P's and
self-evaluation).
I believe, the assignment
strategy helped the students
work independently.
I believe the assignment
completion strategy is likely
to result in permanent
improvement.
Overall, I have a positive
impression 9f the assignment
completion strategy.
Anything you would like to see done differently?
Figure 9
Social Validity Measure for Students.
68
Circle one number for each statement.
Please Read I Agree I Do Not Agree
The assignment worksheet helped 1 2 3 4 5 6
me work on my homework.
I did fine on assignments without 1 2 3 4 5 6
the worksheet.
There are better ways to help me
do my homework than the 1 2 3 4 5 6
assignment worksheet.
I like the assignment worksheet. 1 2 3 4 5 6
I think the assignment worksheet 1 2 3 4 5 6
will help me do better in school.
69
Procedures
The following section outlines the procedures that were used to investigate the
research questions motivating this study. Described below are descriptions of activities
for each phase of the multiple baseline design, data collection procedures, and related
research design procedures.
Baseline phase,
The purpose of the baseline phase was to measure assignment attack behavior
under normal classroom conditions. To adequately define a stable', predictable pattern of
assignment attack for each student, baseline data were collected until future behavior
level and trend could be predicted (Horner et ai., 2005). The length of baseline phases
across participants approached the recommended guideline of ten data points (Horner et
ai.,2005).
Intervention phase,
During this phase, the teacher introduced SAAS into the normal classroom routine
using the script depicted in Appendix A. Assignment attack data were recorded until a
clear change in behavior level, trend, and/or variability was evident. The decision of
whether there was an experimental effect was based on evidence that assignment attack
levels in the intervention phase were outside the range of predicted levels demonstrated
in the baseline. At that point, SAAS was introduced to the next group with assignment
attack data recorded for the participating student.
70
Fading ofexternal supports.
The initial data points of intervention phase were recorded with the 4 P's checklist
in place. When there was evidence assignment attack data had changed, the 4 P's
checklist was replaced with the shorter "first letter cue" checklist for the next 2-3 data
points. After a stable pattem of data was evident, the shortened checklist was removed
according to the script illustrated in Figure 12
Data Collection and Analysis
Assignment attack and on-task behavior were the experimental data collected on a
daily basis. The researcher sat in the back of the classroom prior to the start of the
resource period. Once the special education teacher prompted the students to begin
working on assignments, assignment attack observation began. As soon as the student
demonstrated the first three behaviors on the assignment attack rating scale (i.e., recalling
assignment, gathering materials, and initiating work), scores were assigned for each
domain, deducting points depending on need for teacher support according to the criteria
depicted on the score sheet (see Figure 1). For example, with the "recalling assignments"
domain, the evaluation criteria were as follows:
1 = Teacher must tell student assignment or show student pink
sheet/folder. Student has no recollection of assignment and acts like they
have no work.
2 = Teacher tells student assignment or shows student pink sheet/folder.
Student acts like they know what to do but doesn't know subject and
details.
3 = The student knows about assignment if asked or checks pink
sheet/planner with teacher once, must get repeated teacher clarification
regarding the details of the assignment (Le., teacher pointing to pink
sheet/folder or verbal instructions on multiple occasions).
4 = The student knows both the subject (e.g. science) and the details of
the assignment and demonstrates this by gathering materials, but may
71
ask one clarifying question of teacher. Teacher does not need to review
pink sheet or folder with student.
S = Student requires no support for knowing subject and assignment
details.
Finally, at the end of the period, the whole-class engagement score was recorded
on the assignment attack rating scale. At the end of the class, the four assignment attack
domains were combined to produce a total score, and the percentage of intervals on-task
was calculated.
On-task observation began immediately after the student initiated an assignment.
The observation duration was 10 minutes, unless the student completed the task early or
was asked by the teacher to do something else. On-task behavior was measured in 10
second intervals, and the student was deemed on task ifhe/she maintained engagement
for at least eight seconds during the interval. A digital recording of an electronic timer set
to beep at 10 second intervals was copied onto an MP3 player, and this served as the
interval signal (e.g., Stahr, Cushing, Lane, & Fox, 2006).
All other outcome measures (i.e., the HPC, grades, assignment completion rate,
student completed probes, and the BRIEF) were collected once at the beginning and once
at the end of the study. Classroom grades and assignment completion data were collected
by the special education teacher and reviewed with the researcher. The student-completed
probes measuring motivation, strategy use, and academic self-efficacy were administered
in class by the special education teacher. The BRIEF was sent home to parents and
collected at school by the special education teacher.
72
Inter-rater reliability procedure.
Inter-rater reliability refers to the stability of scores across raters (Primavera,
Allison, & Alfonso, 1997). When two or more researchers use the same measure to
collect data, a significant threat to the internal validity of a study is poor inter-rater
reliability (Gersten et aI., 2005). For this study, inter-rater reliability for both assignment
attack and percentage of intervals on-task was measured using the exact percentage of
agreement. Exact agreement is appropriate when data are non-dichotomous (i.e.,
assignment attack rating scale) and when there are only two observers (Primavera et aI.,
1997). Agreement was calculated by dividing the number of agreements by the total
number of observations. For assignment attack, agreement meant the same cumulative
total, plus or minus two points (e.g., scores of 15 and 17 would be considered in
agreement). Agreement was also calculated for each of the four domains constituting the
cumulative assignment attack score. Exact agreement was defined as the same domain
score, plus or minus one point. For on-task behavior, agreement meant either the same
on-task 01' off-task rating in a ten-second interval.
Obtaining a high degree of agreement between raters using one measure typically
requires a certain amount of training (Kennedy, 2005). Reliability training for this study
took place prior to baseline data collection. During the pilot study, it took three days and
12 student ratings to achieve an inter-observer reliability correlation ofr = .96. For this
study, the researcher and communication disorders and sciences student observed
students in the classroom, completed the assignment attack scale according to the process
described above, and compared ratings for reliability training. Reliability training was
73
completed for ten measures, and exact agreement for assignment ~ttack scores was 90%.
Subsequently, inter-observer agreement was measured for 28% of the total assignment
attack an on-task behavior observations. Agreement for assignment attack was 89%.
Agreement for each of the assignment attack domains was as follows: "Recall" was 93%;
"Gather Materials" was 85%; "Initiation" was 85%; and "Whole-class Engagement" was
88%. Exact agreement for on-task behavior 91 %.
74
CHAPTER IV
RESULTS
This study was designed to evaluate the effects of metacognitive strategy training
on self-regulation of assignment management in a resource setting for adolescents who
qualify for special education. Research questions pertained to student and teacher strategy
implementation (i.e., fidelity, usability, and social validity), assignment attack, and
general impact on academic, behavior, and cognitive variables. This section presents the
findings relative to these research questions under the general headings of
Implementation, Assignment Attack, and Generalized Impact. Table 2 provides a
summary of the study results, listing each dependent variable, corresponding measures,
and direct,ion of related treatment effect.
Implementation
SAAS was implemented as prescribed across students following two days of
instruction provided by the special education teacher. Initial instruction required
approximately 10-15 minutes per day, and SAAS was applied to Adam's classroom first
followed by Brett's and Christina's classes respectively. Subsequently, the special
education teacher provided two additional instruction days for each group, once to
introduce fading of the 4P's checklist and once to instruct students to repeat the 4P's
internally'during assignment completion. Approximately 60 minutes of instruction were
Table 2
Summary ofStudy Results
75
Results/Direction of Effect
Measures. (Research Question)
Implementation (R. Q. 1-3)
Treatment Delivery
Treatment Adherence
Adam
97.1%
77.7%
Brett
96.8%
90%
Christina
100%
100%
N/A
N/A
Social Validity (Teacher)
Social Validity (Student)
Assignment Attack (R. Q. 4)
Assignment Attack
Generalization (R. Q. 5-8)
Classroom Grades
Assignment Completion Rate
Self-regulated Learning Probes (Total)
BRIEF (Global Executive Composite)
Homework Problem Checklist (Teacher)
Homework Problem Checklist (Parent)
% ofIntervals On-Task*
N/A = missing data prevented evaluation of effect.
* = Unable to interpret; failure to demonstrate stable baseline.
+
+
+/-
+/-
+
+
+
+
+/-
+
N/A
+
+
+
+/-
+
+
+
+
+
76
provided for each student across the intervention, fading, and maintenance phases of the
study.
Treatment Fidelity
Observation of fidelity and teacher self-report of fidelity were measured using the
form depicted in Figure 9. Treatment delivery for the special education teacher ranged
from 80% to 100% (M = 97%), and the range of delivery fidelity for one educational
assistant (i.e., Brett's class) was 80% to 100% (M = 96.8%). The treatment delivery
fidelity mean for the other educational assistant (i.e., Christina's class) was 100%.
Fidelity of treatment adherence ranges and average percentages were calculated
for each student: Adam was 33% to 100% (M= 77.7%); Brett was 33% to 100% (M=
89.9%); and Christina was 100%. There were three instances of 33% treatment adherence
between Adam and Brett. The respective teachers were provided feedback following each
of these three sessions to point out the neglected elements. It should be noted these three
days were in the first two weeks of the respective intervention phases and researcher
feedback resulted in subsequent 100% adherence. These results provide affirmative
support for Research Question 1 and 2; the data support a high degree of teacher
implementation and student adherence using the SAAS procedure.
Social Validity
Social validity measures were distributed by the researcher to students and teacher
after the study was completed. Students were asked to complete the measures in class, the
special education teacher was asked to complete measures during a post-intervention
interview. The results from the student-completed measure are presented in Table 3.
77
Table 3
Student Social Validity Data
Student
Question Adam Brett Christina Average
1 3* 6 6 5
2 3 1 2 2
3 3 1 4 2.7
4 4 6 3 4.3
5 4 6 6 5.3
* Each item is rated on a six point Likert-type rating scale ranging from 1 (Agree) to 6 (Do Not Agree).
Student responses indicated they did not perceive the 4P's checklist and related
metacognitive process helped with homework completion or academic success. In
addition, there was agreement across students that they believed another approach would
help them with homework completion and general academic achievement.
Teacher social validity data are presented in Table 4. These findings demonstrated
strong teacher endorsement of SAAS as an effective intervention. The responses indicate
generally positive responses about the usability of the strategy, and the teacher reported
she intends to repeat SAAS at the beginning of next school year. She shared that she
plans on reinforcing the student self-reflection process by posting the 4P's checklist in a
prominent spot in the classroom and asking students to write self-reflective statements at
the end of class. Social validity measures (research question 3) were mixed with weak
endorsement by students and strong endorsement by teachers
78
Table 4
Teacher Social Validity Data
Response
Item
Strongly
Disagree Disagree Neutral Agree
Strongly
Agree
1 X
2 X
3 X
4 X
5 " .. .I am excited to implement it (the 4P's) from the beginning of the year."
Assignment attack
To address research question 4, in-class assignment attack behavior was recorded
over the course of thirteen academic weeks, not counting two weeks for winter break and
one week for spring break. A total of 97 data points were collected across the three
participating students: 31 for Adam, 32 for Brett, and 34 for Christina. There were
seventeen instances when assignment attack data were not collected because the target
behaviors did not occur (seven for Adam and ten for Brett). On these days, assignment
completion performance could not be recorded because no work was attempted. If
students refu.sed to participate in class and the teacher was unable to resolve the situation,
no assignment attack variables could be assessed. Examples when it was not possible to
take data on assignment attack included instances when a student argued he did not have
79
any work to complete, left class and returned without assignment and asked to leave class
again to get ~ssignment, or refused to shift from a non-class activity such as reading a
comic book.
Assignment attack data were interpreted using visual analysis of raw data. For
each phase in the multiple baseline design, assignment attack level, trend, and within-
phase variability were analyzed for each student. Between-phase analysis also included
evaluating change in level, trend, and variability. Additionally, the immediacy of effect,
data overlap, and cross-subject consistency was assessed to determine the quality of the
effect (Kennedy, 2005).
The assignment attack data are presented in Figure 10. Adam and Brett revealed a
clear change in level across phases with high average assignment 'attack during the
intervention, fade, and maintenance phases relative to baseline. Christina's data reveal a
high degr~e of variability and rising trend during the baseline phase, hence interpretation
of intervention effects was not possible. Replication of treatment effects was thus
observed across two of the three students at two different points in time. Guidelines for
interpreting evidence of clear experimental control in single subject research involve
demonstrating replication of effects at three distinct points in time (Homer et aI., 2005).
Adam's baseline data revealed a low and stable baseline with assignment attack
scores ranging between four and eight (M = 5.8). Assignment attack in the intervention
Figure 10
Assignment Attack Data
Adam
Baseline
(1)
....
200
0(f) 18 -
~ 16 -
0
co 14~ 12 -
C 10 -
(1)
E 8 -
c 6 -
0>
'iii 4 -
en
«
Brett
Intervention Fade
t
Maintenance
r
80
(1)
.... 20 -0
0 18 -(f)
~ 16 -
0
co 14 .-
~ 12 -
C 10 -
(1) 8 -E
c 6 -
0>
'iii 4 -
en
«
J
Christina
~ 20 -
0 18 -0(f) 16 -
~
0 14 -co
~ 12 -
C 10 -
(1) 8 -E
c 6·
0>
'iii 4
en ~ ~ ~ ~ ~ ~« ~ ~~ §
/
81
and fade phases was characterized by considerable variability, ranging between six and
twenty, with a slightly delayed but distinct increase in level (M = 14.1). One data point
late in the intervention phase was low (6), but this should be interpreted with caution
since he was'involved in a fight before the class. Adam's assignment attack scores sustain
the high level during the maintenance phase (M= 15.3).
Brett's assignment attack data were slightly higher in level at baseline (M = 8.4)
compared. to Adam's baseline. His data were also more variable, ranging between five
and twelve, and there was a slight downward trend at baseline. There was some data
overlap between Brett's baseline, intervention, and fade phases reflecting a high degree
of variability, but there was a rapid and large increase in level for the intervention and
fade phases (M = 14.8). Brett's assignment attack level remained high during the
maintenance phase (M = 17).
Christina's data reflected a high degree of variability in the baseline phase with
assignment attack scores ranging from four to eighteen. Also, Christina's assignment
attack began to trend up starting at the low on day nine. Despite this trend, her mean
assignment attack score during baseline was 8.6, similar to Brett's baseline average.
Christina's intervention and fade assignment attack levels were consistently high (M =
16.1) with very little variability compared to baseline, with scores ranging from twelve to
nineteen. Due to the unstable pattern during the baseline phase, it was impossible to
evaluate whether introducing SAAS contributed to this increased assignment attack level.
82
Generalized Impact
Data for the generalized impact variables corresponding to research questions 5 -
8 were analyzed using initial and final individual raw scores, initial and final group
means, as well as individual and group difference scores. The utility of inferential
statistics to evaluate initial and final score differences was limited due to the small
number of participants.
Grades
Classroom grades were calculated for each student based on performance at the
end of the first and third grading periods during the academic calendar. At the host
middle school, teachers prepared progress reports four times a year, and the data from
students' social studies and math classes were used to evaluate possible academic impact
of SAAS. The initial and final classroom grade data for the three students are presented in
Table 5..
Classroom grade data indicated an inconsistent pattern between math and social
studies performance. Across all students, the change in average math score was -14.8%
while the change in average social studies grade was +13.1%. All students' math grades
dropped by at least -13% while each students' social studies grade improved, ranging
from +4.9% (Christina) to +26.3% (Brett). Given this inconsisten~ pattern, it is
impossible to determine whether the intervention affected classroom performance since
external factors (i.e., different classroom cultures) are more likely to account for the
performance discrepancy.
83
Assignment Completion Rate
Assignment completion rates for all schoolwork were calculated for each student
based on the proportion of assignments completed divided by the total number of
assignments given in a one-month period near the end of the first and third grading
periods. The special education teacher assembled assignment completion data using
electronic dajabases. The initial and final assignment completion data for the three
students are presented in Table 5.
The average assignment completion rate increased or was unchanged for all
students except Adam in social studies. The average initial assignment completion rate in
math for all three students was 88.3%, and the final average assignment completion rate
for math was 89.3% across all students representing an average increase of 1%. Adam's
math completion rate increased 3% while Brett's and Christina's completion rates were
unchanged. The average initial assignment completion rate in social studies was 78.3%,
and the final "average social studies completion rate was 87.3% across all students
representing an average 9% increase. Brett's and Christina's social studies assignment
completion rates increased 12% and 25% respectively, but Adam's final assignment
completion rate in social studies dropped 10%.
Table 5
Classroom Grades and Assignment Completion Rate.
Academic outcome descriptive data
Students
(n= 3) Math Grade (%) Social Studies Grade (%) Math Completion Rate (%) Social Studies Complp.tion Rate (%)
Initial Final Change Initial Final Change Initial Final Change Initial Final Change
Adam 76 62 -14 69.5 77.8 +8.2 77 80 +3 89 79 -10
Brett 95 77.4 -17.6 49.5 75.8 +26.3 88 88 0 71 83 +12
Christina 85 72 -13 69 73.9 +4.9 100 100 0 75 100 +25
Average 85.3 70.5 -14.8 62.7 75.8 +13.1 88.3 89.3 +1 78.3 87.3 +9
00
+:>.
85
Self-regulated Learning
An attempt to measure the constructs deemed crucial to self-regulated learning
used student responses on the Self-Regulated Learning questionnaire. The constructs of
interest were motivation, strategy use, and academic self-efficacy. Of note, higher scores
on this measure indicated lower perceived strengths in these areas. The participating
students completed the self-regulated learning probes once before the intervention and
once during the maintenance phase. These data are presented in Table 6. The average
cumulative score across all students decreased by 3 points (i.e., 40.3 - 37.3) indicating
slightly improved perception of motivation, strategy use, and self-efficacy. Adam's and
Christina's total score change was both -5, while Brett's score difference was +1. Adam
and Christina reported improvements on motivation and self-effic.acy questionnaire items
with little or no change on strategy use. Brett reported improvements on self-efficacy
items, but indicated negative outcome on motivation and strategy use questions. Across
all students, responses on motivation and self-efficacy items improved (M change = -1.3
and -3 respectively) while responses on strategy use did not improve (M change = +1.3).
Se([-regulation in Non-academic Contexts
The outcome measure used to assess potential changes in self-regulation outside
academic confines was the parent version of the BRIEF. Data from the parent-completed
BRIEF questionnaire are presented in Table 7. For each student, T scores (M = 50; SD =
10) and percentile rankings were calculated for Metacognitive Index (MI), Behavior
Regulation Index (BRI), and Global Executive Composite (GEC) when possible. Change
Table 6
Se(fRegulated Learning Outcome Data and Analysis.
Self-Regulated Learning Probes*
Students
(n = 3)
SRL Motivation (max = 25) SRL Strategy Use (max = 20) SRL Self-Efficacy (max = 20) SRL Total (max = 65)
Initial Final Change Initial Final Change Initial Final Change Initial Final Change
Adam 17 13 -4 12 12 0 16 15 -1 45 40 -5
Brett 12 15 +3 9 12 +3 15 10 -5 36 37 +1
Christina 13 10 -3 11 12 +1 16 13 -3 40 35 -5
Average 14 12.7 -1.3 10.7 12 +1.3 15.7 12.7 -3 40.3 37.3 -3
* Lower scores reflect increase in reported self-regulatory behavior.
00
0\
87
scores were calculated for pre-post-intervention differences for each score, and lower
scores indicate less evidence of dysexecutive symptoms.
Adam's initial BRIEF results showed the MI and GEC both within 1.5 SD of the
sample mean, which is within the clinical cutoffvalue (Gioia et aI., 2000b). Adam's BRI
was exactly on this 1.5 SD cutoff at baseline. No change scores were calculated for Adam
since his post-intervention BRIEF was not returned.
Brett's BRIEF data showed evidence of significant executive function
impairment. Due to missing data (i.e., incomplete questionnaire), no BRI or GEC
difference scores were calculated for Brett. His pre-post-intervention MI difference was
+15 points indicating an increase in perceived metacognitive impairment according to
parental report.
Christina's BRIEF data revealed clinically significant dysexecutive symptoms on
the MI but not the BRI; change scores on these indices were +9 and -8 respectively. Her
pre-intervention GEC indicated evidence of impairment, however Christina's final
BRIEF results show a 15 point improvement, near the average score for the normative
sample.
Behavior
The HPC was completed by teachers and parents as a pre-post- intervention
comparison of perceived student behavior and efficiency while completing homework.
For this measure, lower scores indicated fewer disruptive or acting-out behaviors during
homework. Descriptive statistics for this measure are presented in Table 8. The results
indicated a context-dependent improvement in homework-related behavior as teachers
Table 7
BRIEF Outcome Data and Analysis.
Students
(n = 3)
Adam
Brett
Christina
BRIEF*
Metacognition Index Behavior Regulation Index Global Executive Composite
Initial T Final T Initial T Final T . Initial T Final T
score score Change score score Change score score Change
(%tile) (%tile) (%tile) (%tile) (%tile) (%tile)
58 N/A# N/A# 65 N/A# N/A# 61 N/A# N/A#
(79) (91 ) (84)
68 83 +15 N/N' 95 N/N' N/N' 86 N/N'
(93) (99) (99) (99)
83 92 +9 46 38 -8 66 51 -15
(99) (99) (42) (7) (92) (61 )
* Higher scores indicate greater degree executive function impairment.
/\ Missing data prevented calculating scale scores.
# Final questionnaire not returned.
00
00
89
reported improved behavior while parents did not. Adam's teacher-completed HPC
indicated a slight decrease in disruptive homework behavior. His parent-completed post-
intervention HPC was not returned. Brett's parent HPC score increased by eight points
while his teacher HPC score decreased by 18 points. Christina's teacher-completed HPC
decreased by nine points from 25 to 14, although it should be noted that different teachers
completed the pre- and post-intervention HPC questionnaires which could have affected
the reliability of the scores. Her parent-completed measure decreased by three points.
These findings reveal a clinically significant change in homework behavior as reported
by teachers.
Table 8
Homework Problem Checklist Data
Homework Problem Checklist*
Students HPC-Parent HPC-Teacher
(n = 3) (max = 60) (max = 60)
Initial Final Change Initial Final Change
Adam 38 N/A N/A 44 42 -2
Brett 52 60 +8 32 14 -18
Christina 31/57 28 -3 25 14# -9
Average 40.3 44 +2.5 33.7 28 -9.7
* Lower scores indicate fewer reported behavior problems.
# Initial and final teacher HPC completed by different teachers.
90
On-task Behavior
To evaluate the effect of improved assignment attack on subsequent task
engagement, on-task behavior was measured immediately following assignment
initiation. On-task behavior was measured using interval recording and the data were
mapped onto the multiple baseline design as depicted in Figure 11. Using the guidelines
for interpreting data in single subject research described above, it was evident there was
no functional relation between SAAS and proportion of time students were on task. It
was not possible to evaluate treatment effect because there was no stable baseline. There
was a high degree of variability within and across phases for all three students resulting
in significant data overlap. In addition, there was no change in variability following the
intervention.
While the intervention did not specifically address task engagement, it was
hypothesized that once these students initiated a task they would remain engaged. Visual
analysis of the data indicated this was not the case. Pearson Product-Moment Correlation
coefficients were calculated to evaluate whether on-task and assignment attack measures
were related. These data are presented in Table 9. Consistent with evidence from visual
analysis, the findings indicated no relationship between cumulative assignment attack
scores and the percentage of intervals on-task. Similarly, there was little evidence
assignment attack domains or on-task were related with one exception. The correlation
coefficients between whole class engagement and the proportion of intervals on-task
ranged from r = .433 (Adam) to r = .711 (Brett) indicating a small to moderate
relationship between the measures (i.e., Cohen, 1992).
91
Figure 11
Percentage ofIntervals On Task
Adam
Baseline
-'"~
f- 100 -c:
0
'" 80 -«i
C
OJ 60 -E
"-
0 40 -OJgp
E 20 -
OJ
<.)
....
OJ 0-c...
Intervention Fade
/
Maintenance
Brett
-;:; 100 -
'"f-
c: 80 -0
'"«i
C 60 -
OJ
E
"- 400
OJ
•gp
E 20 -
OJ
<.)
....
OJ
c... o -
Christina
-;:; 100 -
'"f-
c: 80 -0
'" j«iC 60 -Ec:"- 40 -0
•OJ
OJ)
'" 20 -E
OJ
2
<ll 0---- ~_.~c...
I ~ ~ ~ ~ ~ ~ i ~ ~ ~ ~ ! I ~ ~ ~ ~ ~ I ~~~ ~ ~ ~ ~ ~ ~
Table 9
Correlation Coefficients for Assignment Attack Domains and On-Task Data
92
Adam
Recall Materials Initiation Engagement . Total On-task
Recall .886* .504* .633* .878* .056
Materials 1 .344 .546* .795* .186
Initiation 1 .786* .806* .136
Engagement 1 .893* .433*
Total 1 .244
On-task 1
Brett
Recall Materials Initiation Engagement . Total On-task
Recall 1 .701 * .634* .315 .850* -.100
Materials 1 .485* .278 .783* .050
Initiation 1 .498* .845* .301
Engagement 1 .654* .711 *
Total 1 .297
On-task 1
93
Table 9
(cont.) Christina
Recall Materials Initiation Engagement Total On-task
Recall 1 .869* .639* .345 .871 * .195
Materials 1 .662* .421 * .896* .215
Initiation 1 .593* .879* .193
Engagement 1 .675* .599*
Total 1 .334
On-task 1
*P < .05
Additional follow-up analysis was conducted to examine whether there was a
differential effect of the intervention on the first five minutes of on-task behavior
compared to the second five minutes of the observation period. The proportion of
intervals on-task for Adam increased 24% following the intervention during the first five
minutes. Brett's on-task intervals increased by 2%, and Christina's proportion of intervals
on-task also increased by 12% during the first five minutes. This improvement was not
evident in the proportion of intervals on-task during the second five minutes. Adam's
percentage of on-task intervals increased by 20%, but Brett's on-task behavior was
unchanged and Christina's percentage of on-task intervals decreased by 14% during the
second five minutes of the observation period following the intervention. These data
reveal a positive effect across all students on the proportion of intervals on-task during
94
the first five minutes of task engagement. This suggests the primary effect of the strategy
was on helping students initiate tasks.
95
CHAPTER V
DISCUSSION
The goal of this study was to evaluate the effects of metacognitive strategy
instruction op. assignment attack behavior and related academic, behavioral, and cognitive
variables for middle school students who qualify for special education. This study
extended previous research by targeting self-regulation of assignment management and
demonstrating improved assignment attack can be maintained after external supports are
faded. Grounded in social-cognitive theory, we hypothesized that students can learn to
self-regulate assignment attack behavior following systematic instruction of a
metacognitive strategy. A number of related academic, cognitive, and behavioral
variables were measured to evaluate generalized impact of any treatment effects. The
results revealed positive outcomes on research questions pertaining to implementation of
SAAS and effects on assignment attack behavior. The findings reiated to questions
examining the general impact of SAAS on other academic, behavior, and cognitive
variables revealed little effect. The following discussion of these findings follows the
research question outline (i.e., Implementation, Assignment Attack, and Generalized
Impact), including a discussion of the study limitations and future research
considerations.
96
Fidelity ofImplementation in Natural Context
This study demonstrated that the SAAS intervention package can be implemented
by a busy middle school teacher with high fidelity and ease. The three-pronged approach
(i.e., task-specific checklist, systematic instruction, and fading external supports) was
delivered with high fidelity across three different groups of students. The demonstration
of implementation fidelity is critical for evaluating classroom-based interventions since
naturalistic school contexts rarely contain the elements measured in experimental
evaluations of instructional techniques. The SAAS model may provide clinicians a
framework for conceptualizing and implementing metacognitive-based strategies that
transcend contextual barriers.
Student adherence was demonstrated, further supporting the usability of this
instructional method. Exploratory work preceding this study demonstrated that more
complex assignment management strategies are ineffective due to students' inability to
manage the requisite components. Students were able to use SAAS with minimal teacher
input suggesting an intervention framework that may be applied to a wider student
population.
The results ofthis study also indicated high teacher endorsement of metacognitive
strategy instruction as an approach to improve assignment attack. The participating
special education teacher reported high satisfaction with the intervention framework and
its emphasis on student cognitive processes. The teacher stated she was particularly
interested in metacognitive interventions because, despite four separate external supports
for assignment management in her classroom, some students remained dependent on
97
teachers for assignment attack. During the teacher interview, she indicated her belief that
her students' breakdown was at the cognitive processing level, and that SAAS worked
because it affected change at the process level.
While the special education teacher endorsed the social validity of SAAS,
students did not report satisfaction with the strategy. According to the teacher, low
student approval may be attributed to starting the strategy in January instead of at the
beginning of the year. This may have lead to perception that SAAS was "just another
thing" teachers were making students do. It has been shown that students who struggle
academically are more likely to view homework or assignments more negatively than
students who are successful academically (Bryan et aI., 2001). Turning these students
attention to the assignment management process likely triggered an automatic negative
response. Even if students became cognizant of more independent assignment attack, it is
unlikely these students would have attributed the change to learning SAAS let alone
indicating this was a positive outcome.
Improved Assignment Attack
The primary contribution of this study was the demonstration of improved
assignment attack in the classroom with implementation of a metacognitive self-
regulation strategy, SAAS. In previous research (Ness, Sohlberg, & Albin, in
submission), it was evident that student assignment attack could be improved (i.e.,
become less teacher-dependent) by teaching students a classroom-based external aid to
facilitate organization. The assignment attack research question for this study focused on
examining whether students can internalize the assignment attack process using a
98
metacognitive strategy taught with a three pronged intervention approach: task-specific
checklist, systematic instruction, and fading of external supports.
Assignment attack data for Adam and Brett revealed a positive effect of SAAS on
assignment attack. This finding is consistent with extant self-regulated learning literature
supporting the idea that learning cognitive strategies can improve certain academic skills.
As noted in the review of self-regulation literature, struggling learners are often less
skilled at self-monitoring academic performance and lack cognitive control compared to
their peers (e.g., Klassen et aI., 2008). These characteristics contribute to difficulty
managing' increased academic and cognitive demands implicit in the secondary education
setting. This study provides evidence suggesting students' self-regulation of certain
academic behaviors is not static, and students can learn metacognitive processes when
provided a model and systematic instruction.
Christina's data show a similar pattern of improvement, however the variability
and upward baseline trend limit the degree to which this change 'Yas attributable to
SAAS, One plausible explanation of Christina's improvement during baseline is
contextual influences in her study support class. Her study support class was rearranged
following the start of the second academic quarter. Elective class scheduling and changes
in some students' individualized education plans resulted in Christina's study support
class size decreasing from six students to two or three students, depending on the day.
This change in class size had the effect of reducing distractions and increasing the
educational assistant's availability to answer clarifying questions, thereby reducing
demands to self-regulate assignment attack. However, Christina's assignment attack level
99
was consistently high with little variation following intervention, consistent with the
effects seen for Adam and Brett.
The overall impact of improved assignment attack was more independent
organization·and initiation of in-class assignments. The assignment attack measure was
designed to capture behavior at the onset of assignment completion, so improvements on
this metric reveal a positive effect on students' ability to start tasks. This finding was
bolstered by the data demonstrating all three students improved in task engagement
during the first five minutes of the on-task observation interval relative to the last five
minutes.
Generalized Impact
This study demonstrates classroom-based metacognitive strategies can improve
independent assignment attack for students in resource contexts. However, it is notable
that the effects were context and domain specific for these students. Improved assignment
attack did not seem to affect change in any other academic, behavioral, or cognitive
variables. The one exception was teacher reported homework behavior.
All three students demonstrated reduced problem behaviors during study support
class. This is significant when considering the overall impact of improved assignment
attack on student outcome. It is possible that the reduction of problem behavior was due
to changes i~ teacher perception only, rather than actual behavior. Students who require
more teacher direction on recalling assignments, gathering materials, and initiating
assignments may appear to present with more acting-out behaviors.
100
The homework-related behavior improvement was the single consistent indicator
of a positive impact on general outcome measures. Two issues are addressed below that
may produce more robust effects in future studies of classroom-based metacognitive
strategy training: measurement and generalization.
Academic and Behavior Outcome Measures
Assignment attack behavior is an important skill; reports from special education
teachers and parents consistently indicate students who struggle academically lack this
constellation of skills. However, quantifying the relationship between assignment attack
and academic performance proved to be a difficult proposition. A sensitive outcome
measure sampling the real impact of this improvement is necessary before the role of
interventions like SAAS in special education settings can be fully understood.
For this study, academic achievement was a primary measure of the possible
generalized impact of SAAS, and included changes in grades and assignment completion
rate. However, academic achievement is a complex outcome to measure. Many variables
account for academic performance (e.g., difficulty of assessment, teacher instruction, and
student psycho-emotional status). Assignment attack by itself may lack sufficient
influence to either affect change in a short period of time or produce measurable change
on such global outcomes. In addition, it was evident in this study there were unaccounted
variables that affected measmement of academic achievement. For example, there was a
significant disparity between final grades in math and social studies across students.
While students' grades in social studies improved, their math grades dropped
significantly. This finding is perplexing because the average assignment completion rate
101
increased 'across students in both classes suggesting other uncontrolled factors (i.e.,
type/difficulty of assessments/assignments) significantly impacted the academic outcome
variables.
Another measurement issue was evident in the measure of generalized behavior
change: on-task intervals. Intuitively, it seemed likely that when students were
independent with assignment attack they would remain on-task after initiating the
assignment. However, there was no correlation between assignment attack scores and
percentage of on-task behavior. In addition, none of the students' data demonstrated
consistent, predictable behavior in contrast to the patterns evident in assignment attack
data. It seems that the measurement approach, while sensitive to apparent task
engagement, was not specific to the actual target behavior. A possible explanation may
be that on-task measurement depended on excessive inference. For instance, it is feasible
to imagine a .student looking up from his or her work for extended periods contemplating
a homework problem, resulting in a score of 0% on-task for that segment. One potential
solution for reducing inference is to measure productivity instead of student behavior.
Giving students prescribed assignments enables researchers to quantify task engagement,
but this introduces a contrived scenario not reflective of actual classroom circumstances.
Another potential solution to measuring task engagement is to examine teacher behavior.
Students who self-regulate their assignment attack should require fewer teacher prompts
to remain on task. While this is an indirect measure, fewer teacher interactions would
suggest increased self-regulation.
102
Generalized Improvement ofCognitive Processing and Strategy Use
The improvements noted in assignment attack and homework-related behavior did
not generalize to other cognitive domains. Research suggests interventions that provide
intense, repetitive cognitive stimulation affect the most change at the process level for
•
children with cognitive impairments (e.g., Butler & Copeland, 2002). It is evident SAAS
did not provide cognitive stimulation at a sufficient dosage to affect change at the
executive functioning level. While the SAAS scripts mandated daily metacognitive self-
assessment, students were not required to engage in cognitive activities as a part of the
intervention. If interventionists were interested in affecting change at the process level, a
cognitive stimulation element such as attention process training (e.g., Butler et aI., 2008)
could be added to the SAAS model. However, targeting process-level changes seems to
be beyond the scope of metacognitive strategy training, especially when these strategies
are applied to groups of students. It is likely the most effective and efficient use of
interventions like SAAS is for narrowly defined, context-specific targets.
Similarly, students did not report improved strategy use in other academic
contexts as measured by the self-regulated learning probes. This was not an entirely
unexpected result given what we know from applied behavior analysis and instructional
literature about generalization (e.g., Stokes & Baer, 1977). For S1l;ldents to use skills or
strategies in various contexts, interventionists must program for generalization. This
includes training skills using numerous stimuli and practicing strategies in different
contexts. Since the strategy was tailored to in-class assignment attack behavior, the
students were only trained to use the strategy in their respective classrooms. Students
103
were expected to internalize the 4P's of assignment attack for all their assignments, so
multiple examples were scripted for initial instruction. Therefore, this study provides
evidence interventionists can teach students metacognitive strategies to improve
assignment attack, but the effects will likely not generalize without design.
Limitations and Future Directions
Limitations related to measurement and generalization are discussed above.
Additional limitations include the typical challenges of single subject research. The small
sample size limits the external validity of the study to groups of students who closely
resemble the participants of this study. To increase the degree these findings could be
generalized to broader populations, experimental research, preferably capitalizing on
random assignment, is necessary.
This study describes a promising metacognitive strategy training approach
designed to improve behaviors consistent with self-regulated learning. However,
Christina's improvement during the baseline phase limits the degree to which her
assignment attack improvement was attributable to SAAS. To confirm the effect of
SAAS and assignment attacK behavior, a replication study is necessary to demonstrate a
clear functional relation between the variables. One hypothesis for why Christina
improved during baseline is that her study support class size significantly decreased
during the study. A solution to this problem would be selecting classes with larger, more
stable student enrollment.
Additional studies are necessary to demonstrate the utility of SAAS beyond
improving assignment attack behavior. Self-regulated learning is ~ broad construct,
104
theorized to explain student motivation and cognitive control across academic tasks.
Since the effects related to SAAS were domain-specific, future research activities may
include expanding the intervention curriculum and corresponding. measures to capture the
generalized impact of improved self-regulation. Overall academic achievement may not
be sensitive enough to capture generalization, so it may be more fruitful to evaluate
emergence of self-regulation behaviors across settings as an indicator of effect.
Beokaerts and Como (2005) propose combining quantitative measures (i.e.,
questionnaires, observations of overt behavior, etc.) with qualitative measures such as
structured interviews and recording student cognitive-motivation strategies as they work.
This measurement approach has the advantage of sampling both the students' underlying
cognitive-motivation skills and capturing evidence of self-regulation as it is being
generated (Beokaerts & Como, 2005).
Summary
This study evaluated the impact of a metacognitive strategy instruction package
on assignment attack for middle school students in a resource support context. The
intervention was implemented as prescribed which validated the utility of the
instructional approach. The results indicated positive effects on student assignment attack
and improved homework related behavior as perceived by classroom teachers. While
additional research is necessary to examine the overall impact of improved assignment
attack, the findings indicate students can learn to self-regulate certain academic tasks
with systematic metacognitive strategy instruction.
105
APPENDIX A
TEACHER SCRIPT FOR IJ'JITIAL WSTRUCTION
1. Announce time to work on assignments.
"All right everyone, it is time to work on any homework you might have."
2. Motivation-Personal Relevance
"We are going to do something different during homework completion time today. Now
that you are in middle school, you are expected to do school work on your own. Keeping
up on work not only helps you do better in school and helps lots of students feel better
about how they do in school."
"Which assignments are easiest to remember? What assignments do you like best? What
do you think.is the hardest part about keeping up with assignments?"
3. Instruction-DI and Self-Appraisal
"What I want you to think about when it is time to work on assignments is the four P's.
The four P's are 'Planner Check', 'Pick an Assignment', 'Prepare Materials', and
'Proceed'. Here is a worksheet to help you remember the four P's."
3a. Demonstration and (positive example): "So when I tell you it is time to start
working, you check your planner, pick an assignment, prepare materials, and proceed.
Pretend I'm a student in 2nd (3 rd) period and the teacher just told me to take out my
homework. The first thing I do is a Planner Check or pink sheet check. I do a nice job
remembering my assignments when I check my planner when it's time to work. The next
thing I do is Pick an Assignment. I determine which one needs to be completed first and
choose to start working on that one. Next, I Prepare Materials. I work best during class
when my materials are organized and I have everything I need before class. Finally, I
Proceed with my assignment. I feel better about my homework when I start working
without being told. And when I'm working, something I do is check to see how I'm doing
and ask myself "Am I on-task and am I making progress?" If not," I would ask the teacher
for help."
"If I do these things and follow this checklist, it is possible I finish my work quicker
and don't need the teacher to remind me what to do. And if you all practice using the
106
checklist, I don't think you will need a teacher to help you manage your assignments
anymore. To' help you learn to think about how you are doing your work, I am going to
ask you to fill out the check boxes on the worksheet every day. You check whether you
did each step on your own or whether you needed a teacher to teH you what to do."
3b. Check for understanding
"What are the 'Four P's'? After I prepare my materials, do I start talking to my friend?
What do I do? "
6. Reflection and self-evaluation (Give five minutes at the end of the class)
"Now that everyone has worked on assignments, I would like you to think about how you
did with the 4 P's and on your assignments. Did you need help remembering or getting
materials together? Did you start and stay on task? Please tell me by checking the' How
Did I Do?' boxes for today."
Collect and initial checklists.
107
APPENDIXB
TEACHER SCRIPT FOR DAILY PROMPTS
1. Direct Instruction
"I would like everyone to take out your pink sheets while I pass o.ut the 4P's checklist. "
Assessment Questions
a. "Who can tell me what the 4P's stand for?"
b. "Who can tell me why we are using this checklist during homework time?"
*Announce time to begin working after 4P's checklist has been distributed*
2. Self-Reflection Statements
Please read at least one statement during self-evaluation period at the end of class.
a. "Why did I ask you to put either checks or minuses in the boxes?"
b. "While thinking about how you did today, ask yourselves, 'Is there anything I
could have done differently so Ms.. ,. did not have to tell me what to do?'"
c. "When thinking about the Proceed box, as yourself what was it that made you
start working. Was it because you wanted or needed to finish the assignment to
learn or get a good grade, or was it because I told you to do it? People tend to do
better on schoolwork when they choose to do the assignment on their own."
d. "Whether you got checks or minuses, think about how you will earn all checks in
ho'mework support class tomorrow."
108
APPENDIXC
TEACHER SCRIPT FOR FADING 4P'S CHECKLIST
1. Announce time to work on assignments.
2. Give students shortened checklist.
"I would like you to remember the 4 P's while you work on assignments today, and now
we'll see how well you remember them. This checklist only has the letter 'p' for each of
the four steps."
3. Ask one student to recite 4 P's (SRSD Phase 5).
"Can anyone remember all four steps without having the full checklist in front of them?"
Go through an example if students do not recall.
4. Repeat steps 4-7 above. Follow this procedure for two weeks.
5. After two weeks, announce time to work on assignments.
6. Ask one student to recite the 4 P's so everyone remembers (Steps 6 and 7 two days
only).
"You all have done so well using the checklist to keep you engaged in homework that we
are going to remove them today."
7. Ask students to say the 4 P's to themselves while working (S~SD Phase 6).
"You will not have the checklist to look at or fill out the 'How did I do' boxes, so I want
to make sure everyone knows what the 4 P's are. Please say them with me ...
Now today, I want you to say the 4 P's in your head while working on assignments."
Ask students to work on assignments once.
109
BIBLIOGRAPHY
Amato-Zech, N., Hoff, K., & Doepke, K. (2006). Increasing on-task behavior in the
classroom: extension of self-monitoring strategies. Psychology in the Schools, 43,
211-221.
Anderman, E., & Young, A (1994). Motivaiton and strategy use in science: Individual
differences and classroom effects. Journal ofResearch in Science Teaching, 31,
811-831.
Anderson, V., & Catroppa, C. (2005). Recovery of executive skills following paediatric
traumatic brain injury (TBI): A 2 year follow-up. Brain Injury, 19,459-470.
Anesko, K., Schoiock, G., Ramirez, R, & Levine, F. (1987). The 'Homework Problem
Checklist: Assessing children's homework difficulties. Behavioral Assessment, 9,
179-185.
Bandura, A. (1986). Social Foundations ofThought and Action: A Social Cognitive
theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A., Barbaranelli, C., Caprara, G" & Pastorelli, C. (1996). Multifaceted impact
of self-efficacy beliefs on academic functioning. Child Development, 67, 1206-
1222.
Bassi, M., Steca, P., Fave, A, & Caprara, G. (2007). Academic self-efficacy beliefs and
quality of experience in learning. Journal ofYouth and Adolescence, 36,301-312.
Belfiore, P., & Hornyak, R (1998). Operant theory and application to self-monitoring in
adolescents. In D. Schunk & B. Zimmerman (Eds.), Self-Regulated Learning:
From Teaching to Self-Reflective Practice. New York: Guilford Press.
Boekaerts, M. (1997). Self-regulated learning: A new concept embraced by researchers,
policy makers, educators, teachers, and students. Learning and Instruction, 7,
161-186.
Boekaerts, M., & Cascallar, E. (2006). How far have we moved toward the integration of
theory and practice in self-regulation? Education Pscychology Review, 18, 199-
210.
Boekaerts, M., Koning, E., & Vedder, P. (2006). Goal-directed behavior and contextual
factors in the classroom: An innovative approach to the study of multiple goals.
Educational Psychologist, 41,33-51.
Boekaerts, M., Maes, S., & Karoly, P. (2005). Self-regulation across domains of applied
psychology: Is there and emerging consensus? Applied Psychology: An
International Review, 54, 149-154.
110
Bryan, T., Burstein, K., & Bryan, 1. (2001). Students with learning disabilities:
homework problems and promising practices. Educational Psychologist, 36(3),
167-180.
Carr, S., & P.unzo, R. (1993). The effects of self-monitoring of academic accuracy and
productivity on the performance of students with behavioral disorders. Behavioral
Disorders, 18, 241-250.
Cooper, H. (1989). Homework. White Plains, NY: Longman.
Cooper, H., Jackson, K., Nye, B., & Lindsay, J. J. (2001). A Model of Homework's
Influence on the Performance Evaluations of Elementary School Students.
Journal ofExperimental Education, 69(2), 18I.
Cooper, H., Lindsay, J. J., Nye, B., & Greathouse, S. (1998). Relationships among
attitudes about homework, amount of homework assigned and completed, and
student achievement. Journal ofEducational Psychology, 90, 70-93.
Cooper, H., & Nye, B. (1994). Homework for students with learning disabilities: The
implications of research for policy and practice. Journal ofLearning Disabilities,
27,470-479.
Cooper, H., Robinson, J., & Patall, E. (2006). Does homework improve academic
achievement? A synthesis of research 1987-2003. Review ofEducation Research,
76,1-62.
Como, L. (2001). Volitional aspects of self-regulated learning. In B. Zimmerman & D.
Schunk (Eds.), Self-Regulated Learning and Academic Achievement: Theoretical
Perspectives. New York: Lawrence Erlbaum.
Denckla, M. (1996). Research on executive function in a neurodevelopmental context:
application of clinical measures. Developmental Neuropsychology, 12, 5-15.
Epstein, M. H., & Polloway, E. A. (1993). Homework: A comparison of teacher's and
parents' perceptions of the problems experienced by students identified as having
behavioral disorders, learning disabilities, or no disabilities. Remedial & Special
Education, 14(5), 40-50.
Feldman, S., Martinez-Pons, M., & Shaham, D. (1995). The relationship of self-efficacy,
self-regulation, and collaborative verbal behavior with grades: preliminary
findil}gs. Psychological Reports, 77,971-978.
Franklin, R, Gorman, B., Beasley, T. M., & Allison, D. (1997). Graphical Display and
Visual Analysis. In Design and Analysis ofSingle-Case Research (pp. 119-158).
Mahwah, NJ: Lawrence Erlbaum Associates.
Fulk, B., Brigham, F., & Lohman, D. (1998). Motivation and self-regulation: A
comparison of students with learning disabilities and behavior problems.
Remedial & Special Education, 19,300-309.
111
Gajria, M., & Salend, S. (1995). Homework practices for students with and without
learning disabilities: A comparison. Journal ofLearning Disabilities, 28(5), 291-
296.
Gazzaniga, M., Ivry, R, & Mangun, G. (2002). Cognitive Neuroscience: The Biology of
the Mind (2nd ed.). New York: W.W. Norton & Co.
Gersten, R, Fuchs, 1., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. (2005).
Quality indicators for group experimental and quasi-experimental research in
special education. Exceptional Children, 71, 149-164.
Geurts, H. M., Verte, S., Oosterlaan, J., Roeyers, H., & Sergeant, J. A. (2004). How
specific are executive functioning deficits in attention deficit hyperactivity
disorder and autism? Journal ofChild Psychology and Psychiatry, 45,836-854.
Gioia, G., Isquith, P., Guy, S., & Kenworthy, 1. (2000a). Behavior Rating Inventory of
Executive Function. Lutz, Florida: Psychological Assessment Resources.
Gioia, G., Isquith, P., Guy, S., & Kenworthy, L. (2000b). Behavior Rating Inventory of
Executive Function: Professional Manual. Lutz, FL: Psychological Assessment
Resources, Inc.
Gioia, G.; Isquith, P., Kenworthy, 1., & Barton, R (2002). Profiles of everyday executive
function in acquired and developmental disorders. Child Neuropsychology, 8,
121-137.
Graham, S., & Harris, K. (1993). Improving the writing of students with learning
problems: Self-regulated strategy development. School Psychology Review, 22,
169-182.
Graham, S., Harris, K., & Mason, 1. H. (2005). Improving the writing performance,
knowledge, and self-efficacy of struggling young writers: The effects of self-
regulated strategy development. Contemporary Educational Psychology, 30,207-
241.
Gresham, F. (1997). Treatment integrity in single-subject research. In Design and
Analysis ofSingle-Case Research (pp. 93-118). Mahwah, NJ: Lawrence Erlbaum
Associates.
Gresham, F., MacMillan, D., Beebe-Frankenberger, M., & Bocian, K. (2000). Treatment
integrity in learning disabilities intervention research: Do we really know how
treatments are implemented? Learning Disabilities Research & Practice, 15, 198-
205.
Gureasko-Moore, S., DuPaul, G., & White, G. (2007). Self-management of classroom
preparedness and homework: effects on school functioning of adolescents with
attention deficit hyperactivity disorder. School Psychology Review, 36(4),647-
664.
112
Happe, F., Booth, R, Charlton, R, & Hughes, C. (2006). Executive function deficits in
autism spectrum disorders and attention-deficit/hyperactivity disorder: Examining
profiles across domains and ages. Brain and Cognition, 61,25-39.
Harris, K. (1990). Developing self-regulated learners: The role of private speech and self-
instr~ctions. Educational Psychologist, 25,35-49.
Harris, K., Friedlander, B., Saddler, B., Frizzelle, R, & Graham, S. (2005). Self-
monitoring of attention versus self-monitoring of academic performance: effects
among students with ADHD in the general education classroom. The Journal of
Special Education, 39, 145-156.
Hattie, J., Biggs, J., & Purdie, N. (1996). Effects oflerning skills interventions on student
learning: A meta-analysis. Review ofEducation Research, 66, 99-136.
Hill, E. 1. (2004). Evaluating the theory of executive dysfunction in autism.
Developmental Review, 24(2), 189-233.
Hong, E., & Milgram, R (1999). Preferred and actual homework style: A cross-sultural
examination. Educational Research, 41,251-265.
Horner, R, Carr, E., Halle, J., McGee, G., Odom, S., & Wolery, M. (2005). The use of
single-subject research to identify evidence-based practice in special education.
Exceptional Children, 71(2),165-179.
Hughes, C. A., Ruhl, K. 1., Schumaker, J. B., & Deshler, D. D. (2002). Effects of
Instruction in an assignment completion strategy on the homework performance
of students with learning disabilities in general education classes. Learning
Disabilities: Research & Practice, 17(1), 1-18.
Johnson, 1., Graham, S., & Harris, K. R. (1997). The effects of goal setting and self-
in~truction on learning a reading comprehension strategy; A study of students
with learning disabilities. Journal oflearning Disabilities, 30(1),80-91.
Kamann, M. P., & Wong, B. Y. (1993). Inducing adaptive coping self-statements in
children with learning disabilities through self-instruction training. Journal of
learning Disabilities, 26(9),630-638.
Kameenui, E., & Simmons, D. (1990). Designing Instructional Strategies: The
Prevention ofLearning Problems. Englewood Cliffs, NJ: MacMillan.
Kennedy, C. (2005). Single-Case Designsfor Educational Research. New York: Allyn
and Bacon.
Klassen, R., Krawchuk, 1., Lynch, S., & Rajani, S. (2008). Procrastination and
motivation of undergraduates with learning disabilities: A mixed methods inquiry.
Learning Disabilities Research & Practice, 23, 137-147.
Klassen, R., & Lynch, S. (2007). Self-efficacy from the perspective of adolescents with
LD and their specialist teachers. Journal ofLearning Disabilities, 40,494-507.
113
Levin, H., & Hanten, G. (2005). Executive functions after traumatic brain injury in
children. Pediatric Neurology, 33, 79-93.
Lord, S., Eccles, J., & McCarthy, K. (1994). Surviving the junior high school transition:
family processes and self-perceptions as protective and risk factors. The Journal
ojEarly Adolescence, 14, 162-199.
Kameenui, E., & Simmons, D. (1990). Designing Instructional Strategies: The
Prevention ofLearning Problems. Englewood Cliffs, NJ: MacMillan.
Margolis, H., & McCabe, P. (2006). Improving self-efficacy and motivation: What to do,
what to say. Intervention in School and Clinic, 41, 218-227.
Marquis, J., Homer, R., Carr, E., Turnbull, A, Thompson, M., & Behrens, G. (2000). A
meta-analysis of positive behavior support. In R. Gersten, E. Schiller & S.
Vaughn (Eds.), Contemporary Special Education Research. New York: Lawrence
Erlbaum Associates.
Mooney, P., Ryan, J., Uhing, B., Reid, R., & Epstein, M. (2005). A review of self-
management interventions targeting academic outcomes for students with
emotional and behavioral disorders. Journal ofBehavioral Education, 14, 203-
221.
Munk, D.-D., Bursuck, W. D., Epstein, M. H., Jayanthi, M., Nelson, J., & Polloway, E.
A. (2001). Homework communication problems: Perspectives of special and
general education parents. Reading & Writing Quarterly, 17(3), 189-203.
Ness, B., & Sohlberg, M. (2007). An executive funtion-based intervention in a middle
school setting: implementation challenges and considerations. Poster session
presented at the annual meeting ofthe Oregon Speech-language-Hearing
Association, Eugene, OR.
Novak, G., & Pelaez, M. (2004) Child and Adolescent Development. London: Sage
Publications.
Olvera, R. L., Semrud-Clikeman, M., Pliszka, S. R., & O'Donnell; L. (2005).
Neuropsychological deficits in adolescents with conduct disorder and comorbid
bipolar disorder: A pilot study. Bipolar Disorders, 7(1),57-67.
Olympia, D., Jenson, W., Sheridan, S., & Andrews, D. (1994). Using student-managed
interventions to increase homework completion and accuracy. Journal ofApplied
Behavior Analysis, 27, 85-99.
Pajares, F. (2003). Self-efficacy beliefs, motivation, and achievement in writing: A
review of the literature. Reading & Writing Quarterly, 19, 139-158.
Paris, S., & Paris, A. (2001). Classroom applications ofresearch on self-regulated
learning. Educational Psychologist, 36,89-101.
114
Paris, S., & Winograd, P. (2001). The role of self-regulated learning in contextual
teaching: Principles and practices for teacher preparation [Electronic Version].
Center for the Improvement ofEarly Reading Achievement. Retrieved 111412009
from http://www.ciera.orgllibrary/archive/2001-04/0104parwin.htm.
Parker, R., & Hagan-Burke, S. (2007). Useful effect size interpretations for single case
research. Behavior Therapy, 38,95-105.
Pennington, B., & Ozonoff, S. (1996). Executive functions and developmental
psychopathology. Journal ofChild Psychiatry, 37,57-87.'
Pintrich, P., Anderman, E., & Klobucar, C. (1994). Intraindividual differences in
motivation and cognition in students with and without learning disabilities.
Journal ofLearning Disabilities, 27,360-370.
Pintrich, P., & DeGroot, E. (1990). Motivational and self-regulated learning components
of classroom academic performance. Journal ofEducational Psychology, 82,33-
40.
Primavera, L., Allison, D., & Alfonso, V. (1997). Measurement of Dependent Variables.
In Design and Analysis ofSingle-Case Research (pp. 41-89). Mahwah, NJ:
Lawrence Erlbaum Associates.
Reid, R. (1996). Research in self-monitoring with students with learning disabilities: the
present, the prospects, the pitfalls. Journal ofLearning Disabilities, 29(3),317-
331. .
Reid, R., Trout, A., & Schartz, M. (2005). Self-regulation interventions for children with
attention deficit/hyperactvity disorder. Exceptional Children, 71(4), 361-377.
Rucklidge, J. J., & Tannock, R. (2002). Neuropsychological profiles of adolescents with
ADHD: Effects of reading difficulties and gender. Journal ofChild Psychology
an.d hychiatry, 43(8),988-1003.
Rudolph, K., Lambert, S., Clark, A., & Kurlakowshy, K. (2001). Negotiating the
transition to middle school: The role of self-regulatory processes. Child
Development, 72,929-946.
Santangelo, T., Harris, K., & Graham, S. (2007). Using self-regulated strategy
development to support students who have "Trubol giting thangs into werds".
Remedial & Special Education, 29, 78-89.
Schunk, D. (1989). Self-efficacy and cognitive achievement: Implications for students
with learning problems. Journal ofLearning Disabilities, 22, 14-22.
Schunk, D. (1998). Teaching elementary school students to self-r~gulate practice of
mathematical skills with modeling. In D. Schunk & B. Zimmerman (Eds.), Self-
Regulated Learning: From Teaching to Self-Reflective Practice. New York:
Guilford Press.
115
Schunk, D., & Zimmennan, B. (1997). Social origins ofrelf-regulatory competence.
Educational Psychologist, 32, 195-208. .
Seidman, 1. 1., Biedennan, J., Monuteaux, M. C., Valera, E., Doyle, A E., & Faraone, S.
V. (2005). Impact of gender and age on executive functioning: do girls and boys
with and without attention deficit hyperactivity disorder differ
neuropsychologically in preteen and teenage years? Developmental
Neuropsychology, 27(1), 79-105.
Shear, P. K., DelBello, M. P., Rosenberg, H., & Strakowski, S. M. (2002). Parental
reports of executive dysfunction in adolescents with bipolar disorder. Child
Neuropsychology, 8(4),285-295.
Shimabukuro, S., Prater, M., Jenkins, A, & Edelen-Smith, P. (1999). The effects of self-
monitoring of academic perfonnance on students with learning disabilities and
ADD/ADHD. Education and Treatment ofChildren, 22,397-414.
Sikora, D. M., Haley, P., Edwards, J., & Butler, R W. (2002). Tower of London test
perfonnance in children with poor arithmetic skills. Develppmental
Neuropsychology, 21(3),243-254.
Singh, K., Granville, M., & Dika, S. (2002). Mathematics and science achievement:
Effects of motivation, interest, and academic engagement. Journal ofEducational
Research, 95,323-332.
Soderlund, J., Bursuck, B., Polloway, E. A, & Foley, R. A. (1995). A comparison of the
homework problems of secondary school students with behavior disorders and
nondisabled peers. Journal ofEmotional and Behavioral Disorders, 3(3), 150-
155.
Sohlberg, M. M., & Mateer, C. A (2001). Cognitive Rehabilitation: An Integrative
Neuropsychological Approach. New York, NY: Guilford Press.
Stahr, B., Cushing, D., Lane, K., & Fox, J. (2006). Efficacy of a function-based
interyention in decreasing off-task behavior exhibited by a student with ADHD.
Journal ofPositive Behavior Interventions, 8,201-211.
Stokes, T., & Baer, D. (1977). An implicit technology of generalization. Journal of
Applied Behavior Analysis, 10,349-367.
Struyk, 1. R, & Epstein, M. H. (1995). Homework, grading, and testing practices used
by teachers for students with and without disabilities. The Clearing House, 69(1),
50-55.
Sugai, G., Homer, R., Dunlap, G., Hieneman, M., Lewis, T., Nelson, C. M., et al. (2000).
Applying positive behavior support and functional behavioral assessment in
schools. Journal ofPositive Behavior Interventions, 2(3), 131-143.
116
Sugai, G., Sprague, J., Homer, R., & Walker, H. (2000). Preventing school violence: the
use of office discipline referrals to assess and monitor school-wide discipline
interventions. Journal ofEmotional and Behavioral Disorders, 8, 94-102.
Taylor, L. K., Alber, S. R., & Walker, D. W. (2002). The comparative effects of a
modified self-questioning strategy and story mapping on the reading
comprehension of elementary students with learning disabilities. Journal of
Behavioral Education, 11(2), 69-87.
Trautwein, D., & Koller, O. (2003). The relationship between homework and
achievement--Still much of a mystery. Educational Psychology Review, 15, 115-
145.
Trautwein, U, Koller, 0., Schmitz, B., & Baumert, J. (2002). Do homework assignments
enhance achievement? A multilevel analysis in 7th grade mathematics.
C~ntemporaryEducational Psychology, 27, 26-50.
Trautwein, D., Ludtke, 0., Schnyder, I., & Niggli, A. (2006). Predicting homework
effort: Support for a domain-specific multilevel homework model. Journal of
Educational Psychology, 98, 438-456.
Dberti, H., Mastropieri, M. A., & Scruggs, T. E. (2004). Check if off: Individualizing a
math algorithm for students with disabilities via self-monitoring checklists.
Intervention in School and Clinic, 39, 269-275.
Dsher, E., & Pajares, F. (2006). Sources of academic and self-regulatory efficacy beliefs
of entering middle school students. Contemporary Educational Psychology, 31,
125-141.
Valiente, C., Lemery-Chalfant, K., Swanson, 1., & Reiser, M. (2008). Prediction of
children's academic competence from their effortful control, relationships, and
classroom participation. Journal ofEducational Psychology, 100, 67-77.
van Mourik, R., Oosterlaan, J., & Sergeant, J. A. (2005). The Stroop revisited: A meta-
analysis of interference control in AD/HD. Journal ofChild Psychology and
Psychiatry, 46(2), 150-165.
Vollmeyer, R., & Rheinberg, F. (2006). Motivational effects on self-regulated learning
with different tasks. Educational Psychology Review, 18,239-253.
Willcutt, E., Doyle, A. E., Nigg, J., Faraone, S. V., & Pennington, B. (2005). Validity of
the executive function theory of attention deficitlhyperactivity disorder: a meta-
analytic review. Biological Psychiatry, 57, 1336-1346.
Willcutt, E., Pennington, B., Olson, R., Chabildas, N., & Hulslander, J. (2005).
Neuropsychological analyses of comorbidity between reading disability and
attention deficit hyperactivity disorder: in search of the common deficit.
Developmental Neuropsychology, 27, 35-78.
117
Wolters, C. (.1999). The relation between high school students' motivational regulation
and their use of learning strategies, effort, and classroom performance. Learning
and Individual Differences, 11, 281-299.
Wood, D. A., Rosenberg, M. S., & Carran, D. T. (1993). The effects of tape-recorded
self-instruction cues on the mathematics performance of students with learning
disabilities. Journal oflearning Disabilities, 26(4), 250-258, 269.
Ylvisaker; M., & DeBonis, D. (2000). Executive function impairment in adolescence:
TBI and ADHD. Topics in Language Disorders, 20(2),29-57.
Zimmerman, B. (1990). Self-regulated learning and academic achievement: an overview.
Educational pfjychologist, 25(1),3-17.
Zimmerman, B. (1998). Developing self-fulfilling cycles of academic regulation: An
analysis of exemplary instructional models. In D. Schunk & B. Zimmerman
(Eds.), Self-Regulated Learning: From Teaching to Self-Reflective Practice. New
York: Guilford Press.
Zito, J., AdkIns, M., Gavins, M., Harris, K., & Graham, S. (2007). Self-regulated strategy
development: Relationship to the social-cognitive perspective and the
development of self-regulation. Reading & Writing Quarterly, 23, 77-95.