USING SELF-MONITORING TO INCREASE ON-TASK BEHAVIOR IN A
JUVENILE JUSTICE FACILITY
A DISSERTATION
SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
IN THE GRADUATE SCHOOL OF THE
TEXAS WOMAN’S UNIVERSITY
DEPARTMENT OF TEACHER EDUCATION
COLLEGE OF ARTS AND SCIENCES
BY
RACHEL LIVELY B.A., M.ED.
DENTON, TEXAS
AUGUST 2019
Copyright © 2019 by Rachel Lively
iii
DEDICATION
To my parents, Joe and Rhonda, who instilled in me a passion for education and life.
Thank you for your endless love, sacrifice, and support.
iv
ACKNOWLEDGMENTS
There are countless individuals who contributed to this dissertation. I would like
to thank my committee chair and advisor, Dr. Diane Myers, for her thorough feedback,
endless support, and for reminding me that there is always a place for humor in the world
of education. Thank you for always entertaining our seemingly endless shenanigans and
for making each of us feel like rock stars. Dr. Jane Pemberton always greeted me with a
smile and made me feel welcome. Dr. Minkowan Goo provided unique insight and
sound advice. Thank you also to Dr. Mary Donelson and Dr. Teresa Starrett for your
personal counsel, support, and encouragement throughout my academic journey.
To my parents, thank you for always believing in me when I didn’t believe in
myself. Your countless sacrifices provided me the opportunity to become a strong and
independent woman who thrives on life’s challenges. Your endless love and support
guided me on a path to the highest level of academic attainment and for this I am
eternally grateful. To my mom, thank you for never letting me quit and always pushing
me to be my best self. To my dad, thank you for the countless hours of cat sitting and
early morning coffee runs.
I would like to acknowledge the encouragement and support of my friends and
colleagues. Many adventures were missed during this journey; however, each of you
graciously understood and remained by my side. Whether it was a needed laugh or an
open ear, each was an integral part of this process. I would like to give special thanks to
Ed Steffek for being my dissertation battle-buddy. Finally, I would like to thank my cat,
Penelope, for always offering to help me type even when I didn’t need her assistance.
v
ABSTRACT
RACHEL LIVELY
USING SELF-MONITORING TO INCREASE ON-TASK BEHAVIOR IN A
JUVENILE JUSTICE FACILITY
AUGUST 2019
Limited research exists on the use of self-monitoring interventions in the juvenile
justice setting. This replication study used a randomized multiple-baseline design across
participants to determine if there is a functional relationship between the self-monitoring
intervention and an increase in on-task behavior of three male students in a secure
juvenile facility in Texas. The self-monitoring intervention consisted of a student
worksheet to track behavior, a vibrating watch to remind students to record their
behavior, and individual training in how to use the worksheet and watch. Results
indicated that self-monitoring was associated with an increase in on-task behavior across
all participants. Also discussed are implications of these findings and future directions
for similar research in the juvenile justice setting.
vi
TABLE OF CONTENTS
Page
DEDICATION ........................................................................................................... iii
ACKNOWLEDGMENTS .......................................................................................... iv
ABSTRACT ................................................................................................................. v
LIST OF TABLES .................................................................................................... viii
LIST OF FIGURES .................................................................................................... ix
Chapter
I. INTRODUCTION .................................................................................................... 1
Statement of the Problem .................................................................................... 3
Research Question .............................................................................................. 4
II. REVIEW OF THE LITERATURE ......................................................................... 5
Juvenile Justice System....................................................................................... 5
Emotional and Behavioral Disorders ................................................................ 10
Self-Monitoring................................................................................................. 16
Rationale for the Current Study ........................................................................ 25
III. METHODOLOGY .............................................................................................. 28
Setting ............................................................................................................... 28
Participants ........................................................................................................ 31
Independent Variable ........................................................................................ 34
Dependent Variable .......................................................................................... 34
Social Validity Measure .................................................................................... 35
Procedures ......................................................................................................... 36
IV. RESULTS ............................................................................................................ 42
Overview of Results ....................................................................................... 42
Student 1 ................................................................................................. 42
Student 2 ................................................................................................. 43
Student 3 ................................................................................................. 44
Intervention Fidelity ................................................................................ 46
vii
Treatment Integrity ................................................................................. 47
Social Validity......................................................................................... 47
V. DISCUSSION ....................................................................................................... 50
Limitations ........................................................................................................ 50
Implications....................................................................................................... 51
Summary ........................................................................................................... 53
REFERENCES .......................................................................................................... 55
APPENDICES
A. Data Collection Sheet .......................................................................................... 75
B. Fidelity Checklist ................................................................................................ 77
C. Self-Monitoring Intervention Social Validity Survey: Student Version ............. 79
D. Self-Monitoring Intervention Social Validity Survey: Teacher Version ............ 81
E. Student Self-Monitoring Sheet ............................................................................ 83
viii
LIST OF TABLES
Table Page
1. Demographic Characteristics of Participants ............................................................31
2. Academic and Behavioral Characteristics of Participants ........................................33
3. Academic and Behavioral Results of Participants ...................................................46
4. Social Validity Rating by Participants ......................................................................48
ix
LIST OF FIGURES
Figure Page
1. Participants’ Percentage of Intervals with On-Task Behavior ..................................45
CHAPTER I
INTRODUCTION
Incarcerated juveniles, who are often transient within the juvenile justice system
and arrive at facilities with varying academic and behavioral needs, present special
challenges to the delivery of a comprehensive, appropriate education (Sedlak &
McPherson, 2010). Many students in the juvenile justice system have one or more
disabilities or mental illnesses (Gagnon, Barber, Van Loan, & Leone, 2009; Krezmien,
Mulcahy, & Leone, 2008), as well as histories of intense trauma and neglect (Ford,
Chapman, Connor, & Cruise, 2012; Lampron & Gonsoulin, 2013). Research indicates
that students involved in the juvenile justice system often exhibit significant deficits in
academic achievement, placing them several years below grade level in reading and math
(Beyer, 2006; Leone & Weinberg, 2012; O'Brien, Langhinrichsen-Rohling, & Shelley-
Tremblay, 2007). For example, Beyer (2006) analyzed developmental assessment data
from 50 incarcerated juveniles and found that the juveniles had difficulty maintaining
appropriate attention, anticipating cause and effect, asking for help from adults, and
digesting information. Additionally, juvenile offenders often experience increased
academic failure, school suspension and expulsion, and are at greater risk of dropping out
of school (Pyle, Flower, Fall, & Williams, 2016).
Despite these academic and behavioral challenges, education plays a pivotal role
in recidivism rates among juvenile offenders. Blomberg, Bales, Mann, Piquero, and Berk
(2011) conducted a longitudinal study involving 4,147 juveniles across 115 juvenile
facilities in Florida. Results indicated that juveniles with higher levels of academic
achievement (e.g., increase number and percentage of high school credits) while involved
in the juvenile justice system were more likely to return to school and less likely to
reoffend within 12 and 24 months after release. The researchers concluded that there is a
link between academic achievement, post-release schooling, and rearrests (Blomberg,
Bales, Mann, Piquero, & Berk, 2011).
Incarcerated juveniles and students with emotional and behavioral disorders
(EBD) often present with similar educational and behavioral characteristics (Mathur &
Schoenfeld, 2010). Students with EBD often exhibit maladaptive behaviors (e.g.,
noncompliance, impulsive behaviors, aggression, lack of attention, avoidance, and
atypical response to typical situations) that impede their academic engagement and lead
to increased risk of suspension, expulsion, and dropout (Chiu, Carrero, & Lusk, 2017;
Cook, Rao, & Collins, 2017). In addition to low academic performance, students with
EBD often lack the social skills needed to form and maintain meaningful relationships
with peers and adults (Ryan, Pierce, & Mooney, 2008).
Like many students in the juvenile justice system, students with EBD often
exhibit deficits in executive function including problem solving, attentional shift,
planning, and self-regulation (Barkley, 2006; Hummer et al., 2011; Mueller & Tomblin,
2012). These deficits in executive functioning, specifically self-regulation, affect
academic instruction. Research suggests teachers devote much of the instructional day to
managing student behaviors (Wehby, Lane, & Falk, 2003). One way to shift behavior
3
management from the teacher to the student is to provide students with the skills needed
to be self-sufficient learners who can monitor their own behavior as a means of
increasing academic outcomes (Rafferty, 2010). Students may be able learn to use self-
monitoring techniques to increase or decrease the likelihood of specific target behaviors.
Self-monitoring interventions have been found to be effective at increasing desired
behaviors, social skills, and academic achievement in students with EBD (Ennis, Harris,
Lane, & Mason, 2014). Because deficits in executive functioning, specifically self-
regulation, impact behavior control and academic performance, juvenile justice facilities
should implement behavioral interventions that require students to monitor and generalize
their appropriate behaviors (Barkley, 2006).
Statement of the Problem
Research indicates that self-monitoring interventions can improve on-task
behavior for students with challenging behaviors across grade levels (i.e., elementary,
middle, and high school). While EBD is overrepresented in the juvenile justice system,
little research exists on the use of classroom-based self-monitoring within the juvenile
correctional school setting. In a pilot study, Lively, Myers, and Levin (2019) examined
the effects of self-monitoring on the on-task behavior of three male students in a secure
juvenile facility. Results indicated the self-monitoring intervention increased on-task
behavior across all participants. Despite promising results, much of the current research
regarding self-monitoring in the juvenile justice system focuses on trauma-based
interventions outside of the educational setting (e.g., Ford & Blaustein, 2013). As
previously mentioned, both students with EBD and those involved in the juvenile justice
4
system often experience deficits in self-regulation (Hummer et al., 2011; Mueller &
Tomblin, 2012), which increases their risk of continued criminal behavior and recidivism
(Fine, Baglivio, Cauffman, Wolfe, & Piquero, 2018). Researchers should further
investigate the effectiveness of self-monitoring on desired behavioral and academic
outcomes of student involved in secure juvenile facilities.
Research Question
This study is a replication of Lively et al. (2019). When evaluating the pilot
study, we recommended that future studies include a formal measure of treatment
integrity during all study phases, which was done in the current study. The purpose of
this replication study was to investigate the effects of self-monitoring training on the on-
task behavior of students who are incarcerated in a juvenile justice facility. This study
contributes to the current literature base on effective practices for educating youth in the
juvenile justice system, the effectiveness of self-monitoring, and the ability of teachers to
implement an efficient intervention that is effective with students with a wide range of
behaviors and disabilities. This replication study investigated the following research
question: Is there a functional relationship between the use of self-monitoring and an
increase in the on-task behavior of adolescent males incarcerated in a juvenile justice
facility?
CHAPTER II
REVIEW OF LITERATURE
Juvenile Justice System
The number of juveniles held in residential placements in the United States fell
44% between 2000 and 2015 (Office of Juvenile Justice and Delinquency Prevention
[OJJDP], 2019). However, in 2015, there were still approximately 48,000 juveniles in
residential placements (Sickmund, Sladky, Kang, & Puzzanchera, 2017). Juveniles in
residential facilities have histories of poor academic performance and school attendance
(Leone & Weinberg, 2010). Additionally, many of these juveniles have experienced
neglect, abuse, homelessness, and trauma prior to entering the juvenile justice system
(Lampron & Gonsoulin, 2013).
Data suggest that minorities are disproportionately represented in the juvenile
justice setting (Lampron & Gonsoulin, 2013). Although Black and Hispanic adolescents
comprise 13% and 17% (respectively) of the adolescent population in the United States,
Black and Hispanic adolescents comprise 39% and 23% (respectively) of juveniles in
residential placements in 2013 (Sickmund et al., 2015). Males are also more likely than
females to be committed to a juvenile facility, accounting for 86% of juvenile inmates in
2013 (Sickmund et al., 2015).
There is also disproportionate representation within the special education
population. Black students are 43% more likely and Hispanic students are 17% more
likely than White students to be identified for special education (Mallett, 2014). The
number of students found eligible for special education services is higher in the juvenile
justice system than in the traditional school setting; prevalence rates range from 9-11% in
public schools and from 34-45% in the juvenile justice setting (Krezmien et al., 2008;
Quinn, Rutherford, Leone, Osher, & Poirier, 2005). Research suggests that two-thirds of
students receiving special education services in juvenile facilities are classified as having
EBD (Kessler et al., 2006; Leone & Wruble, 2015; Visser, Lesesne, & Perou, 2007). One
explanation is that students with EBD have unresolved academic and behavioral issues
with long histories of academic failure and behavior problems (Mathur, Griller Clark,
LaCroix, & Short, 2017). Without appropriate interventions, students with EBD are at
greater risk for involvement in the juvenile justice system (Mathur & Griller Clark,
2013). Students receiving special education services are more likely than peers without
disabilities to experience academic failure, school suspension, and expulsion (Krezmien
et al., 2008; Lampron & Gonsoulin, 2013; Sheldon-Sherman, 2013). Additionally,
school dropout rates are as high as 75% for young offenders (Risler & O'Rourke, 2009;
Tyler & Loftstrom, 2009).
Academic Interventions
Research indicates that students involved in the juvenile justice system exhibit
significant deficits in reading and math and are at greater risk of academic retention
(Harris, Baltodano, Jolivette, & Mulcahy, 2009; Krezmien et al., 2008; Leone &
Weinberg, 2012). Both the Office of Juvenile Justice and Delinquency Prevention and
U.S. Department of Education highlight education as a critical component of the
7
rehabilitation process (National Council on Disabilities, 2003). Considering the impact
of education on recidivism, it is important to improve academic outcomes of students in
the juvenile justice system (Wexler, Pyle, Flowers, Williams, & Cole, 2014). Several
studies on reading interventions in the juvenile justice setting have yielded positive
results. For example, Houchins et al. (2008) examined the use of Corrective Reading on
students with EBD across three juvenile corrections facilities. Results indicated that a
majority of the participants made gains in all reading measures (i.e., reading
comprehension, oral reading fluency, and decoding). Wexler, Reed, Barton, Mitchell,
and Clancy (2018) found similar results when they used a multiple baseline design across
participants to determine if there was a functional relationship between a supplemental
peer-mediated reading intervention and juvenile offenders’ abilities to generate main idea
statements about informational text. Students received explicit teacher instruction on
using the Get the Gist strategy followed by peer-mediated practice. Results indicated a
moderately positive relationship between the reading intervention and main idea
generation (Wexler et al., 2018).
In a pretest-posttest control group design study, Warnick and Caldarella (2016)
found similar positive results while examining the effectiveness of a multisensory
phonics-based reading intervention program on the reading skills of students in a
residential treatment facility in the western United States. Both male and female students
between 13 and 17 years old participated in the study. Students in the treatment group
participated in an 8-week, 38-hr reading program utilizing materials from Spelling and
Reading with Riggs (McCulloch, 1995). Based on pretest and posttest data, students in
8
the treatment group showed significantly larger reading gains than those in the control
group. The researchers concluded that the use of a multisensory phonics-based program
would be an effective intervention for targeting word-level difficulties on juvenile
offenders (Warnick & Caldarella, 2016).
Shippen, Morton, Flynt, Houchins, and Smitherman (2012) found similar results
after examining the effects of the Fast ForWord computer-based reading program on
reading and spelling abilities on juvenile offenders. Results indicated a slight increase in
reading level for students in a juvenile justice facility (Shippen, Morton, Flynt, Houchins,
& Smitherman, 2012). Another study of a computer-based reading program (i.e., Tune
in™ to Reading) was associated with improved instructional reading levels for students
with disabilities in a juvenile justice setting (Calderone, Bennet, Homan, Derick, &
Chatfield, 2009). Additional research is needed to determine the effectiveness of
evidence-based practices on students’ academic deficits in the juvenile justice setting
(Wexler et al., 2014).
Positive Behavior Interventions and Supports
The juvenile justice system has historically focused on a punitive approach to
behavior (Read & Lampron, 2012); however, as indicated in the Guiding Principles for
Providing High-Quality Education in Juvenile Justice Secure Care Settings (U.S.
Department of Education and U.S. Department of Justice, 2014), there has been an
increased focus on promoting a proactive approach to behavior and providing a high-
quality education that “encourages the necessary behavioral and social support services
that address the individual needs of all youths, including those with disabilities and
9
English learners” (p. iv). Specifically, research indicates that positive behavioral
interventions and supports (PBIS) is an effective alternative to a punitive model (Jolivette
& Nelson, 2010). PBIS is a multi-tier framework that incorporates individual needs
assessments and data-driven interventions to increase desired student outcomes by
providing evidence-based tools for program implementation (Horner et. al., 2009; Myers
& Farrell, 2008; Simonsen & Sugai, 2013). The PBIS framework offers instructional and
intervention supports for students with and without disabilities (Algozzine et al., 2012;
Sugai & Horner, 2009). Approximately 80% of students respond positively to primary
tier (i.e., universal) interventions, 10-15% require secondary tier (small group)
interventions, and 5-7% require individualized intervention provided by tier three
supports (Lewis & Sugai, 1999; Sugai & Horner, 2002, 2006).
The research base on PBIS in the juvenile justice system continues to grow as
trends shift from punitive-based, reactive discipline to a more proactive, preventative
approach (e.g., Lampron & Gonsoulin, 2013; Lopez, Williams, & Newsom, 2015;
Simonsen, Pearsall, Sugai, & McCurdy, 2011). Johnson et al. (2015) examined the
impact of school-wide PBIS (SW-PBIS) on improvements in behavioral outcomes (i.e.,
total incidents, incident report without security referral, incident report with security
referral but without security admission, and incident report with security referral and
admission) and academic performance (i.e., average daily attendance and industry
certifications) in a secure juvenile facility in Texas by comparing data from one year
prior to SW-PBIS implementation to one year after implementation. Results indicated a
reduction in total incidents (46%), incident reports without security referral (41%),
10
incident reports with security referral but without security admission (56%), and incident
reports with security referral and admission (35%). Additionally, academic performance
increased concurrently with the implementation of SW-PBIS. There was a 21% increase
in average daily attendance and an increase of 131 industry certifications earned. Results
indicated that the implementation of SW-PBIS increased desired behaviors and academic
engagement (Johnson et al., 2015).
Similar results occurred with secondary tier interventions (e.g., check-in/check-
out, self-monitoring, and social skills training) which have been associated with
improvements in target behaviors such as time on task, disruption, and aggressive
language (Bruhn, Lane, & Hirsch, 2014). Additionally, secondary tier interventions have
been found effective across various settings, including alternative education settings
(Swoszowski, Jolivette, & Fredrick, 2013), residential facilities (e.g., Ennis, Jolivette,
Swoszowski, & Johnson, 2012; Swoszowski, Jolivette, Fredrick, & Heflin 2012) and
secure juvenile facilities (e.g., Caldwell & Joseph, 2012). Because students in the
juvenile justice system often struggle with task completion, organization, and self-
regulation (Moilanen, 2007), additional research is needed to determine the effects of a
targeted secondary tier intervention, specifically self-monitoring, on increasing on-task
behavior and academic performance in the juvenile justice system.
Emotional and Behavioral Disorders
As previously mentioned, students with EBD are represented at a higher rate in
the justice system than in the traditional school setting. Students with EBD commonly
exhibit noncompliance, impulsivity, inattentiveness, disorganization, acting out, and
11
aggression (Landrum & Sweigart, 2014). These externalizing behaviors often interfere
with academic performance and skills acquisition in the general education setting
(Harrison, Bunford, Evans, & Owens, 2013). Similar to students involved in the juvenile
justice system, students with EBD often have poor academic outcomes, with as many as
58% performing below grade level in reading and 93% below grade level in math
(Greenbaum et al., 1996). Nelson et al. (2004) speculated that increased deficits in math
performance in high school correlates with deficits in reasoning skills. In addition to
deficits in reading and math, students with EBD experience more disciplinary action
(e.g., suspension and expulsion) than students in other disability categories (Wagner,
Kutash, Duchnowski, Epstein, & Sumi, 2005). Because students with EBD often fail to
attain basic academic and functional skills, they experience some of the lowest
graduation rates (51.1%) and highest dropout rates (38.1%) of any disability category
(U.S. Department of Education, 2014). Additionally, students with EBD experience
increased unemployment after leaving high school (Andrews, Houchins, & Varjas, 2017).
In order to address these concerns, schools are encouraged to increase the use of
evidence-based practices and interventions for students with EBD (Seeley, Severson, &
Fixsen, 2014). Ryan et al. (2008) reviewed and summarized the literature addressing the
use of evidence-based interventions to target academic performance of students with
EBD. Results were clustered into three categories: peer-mediated (e.g., peer modeling,
peer tutoring, and cooperative learning), self-mediated (e.g., self-monitoring, self-
evaluation, and goal setting), and teacher-mediated interventions through manipulation of
antecedents (e.g., modeling, verbalization of math problems, and mnemonic instruction)
12
and/or consequences (e.g., token reinforcement and written feedback). Overall, all three
intervention categories were associated with positive gains related to the academic
performance of students with EBD in the public school setting (Ryan et al., 2008).
As previously mentioned, students with EBD often experience increased rates of
suspension and expulsion (Wagner et al., 2005) and are at greater risk for involvement in
the juvenile justice system (Mathur & Griller Clark, 2013). Tobin and Sprague (2000)
reviewed and summarized literature on research-based practices for students with EBD in
alternative settings. Tobin and Sprague (2000) identified eight effective research-based
strategies: (a) small teacher-student ratio, (b) structured classroom management, (c)
positive behavior management, (d) adult mentors, (e) functional assessments, (f) social
skills training, (g) instructional strategies focusing on academic deficits, and (h) parent
training programs. Tobin and Sprague (2000) suggested that schools provide evidence-
based interventions to address the varied academic and behavioral needs of students with
EBD.
Interventions for Writing
Research indicates a direct link between language development, reading skills,
and writing ability; for example, as reading comprehension and language fluency
increase, so too does written performance (Berninger et al., 2006). When comparing
writing performance of students with EBD to their peers without disabilities, Gage et al.
(2014) determined that more than 50% of the variance in writing ability in their
participants was linked to reading performance. Because students with EBD experience
13
deficits in reading, they have a diminished opportunity to demonstrate and acquire
knowledge of the writing process.
Self-regulated strategy development (SRSD) is an evidence-based intervention
designed to address deficits in writing while reinforcing self-regulation and motivation
(Harris, Graham, & Mason, 2003). Ennis (2016) used a multiple probe, multiple baseline
design across participants to examine the effectiveness of SRSD to teach summary
writing in social studies to three high school students in a residential facility for youth
with EBD. SRSD lessons were taught using TWA (Think before reading, think While
reading, think After reading) and PLANS (Pick goals, List ways to meet goals, And,
make Notes and Sequence notes). Results indicated a functional relationship between
SRSD instruction and writing performance as measured by summary elements, quality,
and total written words (Ennis, 2016).
Mason, Kubina, Valasa, and Cramer (2012) found similar results using SRSD for
persuasive quick writing on five seventh- and eighth-grade students in an alternative
school for students with EBD. Similar to the SRSD instructional approach used by Ennis
(2016), the researchers utilized SRSD instruction for POW (Pick my ideas, Organize my
notes, Write and say more) and TREE (Topic sentence, Reasons - three or more, Explain,
Ending) for improving persuasive writing skills. A graduate student provided one-on-one
SRSD instruction to each participant. All stages of SRSD strategy acquisition and self-
regulation procedures were employed during the lessons. Results indicated that the
implementation of the SRSD instruction for the POW + TREE intervention increased
persuasive writing skills across all five participants (Mason et al., 2012).
14
Interventions for Reading
In addition to deficits in writing, students with EBD also struggle with grade-level
reading performance; reading deficits are likely to impact life outcomes including
academic success, dropout rate, and employment (Kostewicz & Kubina, 2008; McKenna,
Kim, Shin, & Pfannenstiel, 2017). Students with EBD are often 5 to 7 years behind grade
level in reading (Wei, Blackorby, & Schiller, 2011). In recognition of the importance of
reading as related to student success, researchers and policymakers are giving attention to
the literacy development of students with EBD. Common effective interventions include
context mapping for vocabulary and reading comprehension (Palmer, Boon, & Spencer,
2014; Stone, Boon, Fore, Bender, & Spencer, 2008), corrective reading for reading
comprehension, decoding, and oral reading fluency (Houchins et al., 2008; McDaniel,
Houchins, & Terry, 2013), and repeated reading for reading comprehension and oral
reading fluency (Vostal & Lee, 2011).
For example, Escarpio and Barbetta (2016) used an alternating treatment design to
examine the effects of three reading conditions (i.e., repeated readings, non-repeated
readings, and equivalent non-repeated readings) on the reading fluency, errors, and
comprehension of four sixth-grade students with EBD who were also identified as
struggling readers. During the first phase (i.e., repeated readings) of the study, each
participant read a 100 or 150-word passage three times. If an error occurred, the
researcher immediately read the word and the participant would repeat the word and the
sentence. At the conclusion of each reading, the participant would read aloud any words
that required correction. During non-repeated readings, participants completed a process
15
identical to the previous stage except the passage was only read one time. Procedures in
the third phase were identical to the second phase except that participants read a 300- or
450-word passage one time. Researchers administered both an oral comprehension and
reading fluency assessment at the conclusion of each phase. Results indicated that across
all participants repeated reading had a greater effect on reading fluency, errors, and
comprehension than non-repeated reading and equivalent non-repeated reading (Escarpio
& Barbetta, 2016).
Interventions for Math
In addition to deficits in reading and writing, students with EBD experience
deficits in math and perform significantly below the national average (Riccomini, Witzel,
& Robbins, 2008). Research suggests a number of evidence-based interventions that
increase math outcomes in students with EBD. For example, Lee, Lylo, Vostal, and Hua
(2012) examined the effects of high-preference sequences (i.e., a series of tasks with a
high probability of completion and positive reinforcement) on both digits correct per
minute and latency to initiate nonpreferred problems for three participants in a chartered
alternative school for students with EBD. After demonstrating a preference for a specific
type of problem, as measured by worksheet selection, participants completed high-
preference problems followed by nonpreferred problems. Results indicate that latency
for attempting nonpreferred problems decreased across all participants; however, there
was no effect on accuracy (Lee et al., 2012).
Mulcahy and Krezmien (2009) also examined math accuracy in students with
EBD. Researchers examined the effects of a contextualized instructional package on
16
teaching area and perimeter to four students in a self-contained classroom for middle-
school students with EBD. The instructional intervention consisted of 11 lessons focused
on procedural and conceptual knowledge of area and perimeter using problem-solving
strategies and a self-monitoring technique. Results indicated that math accuracy in both
area and perimeter increased across all participants; however, transfer and maintenance
results were mixed. Additionally, each participant completed the self-monitoring
checklist at the end of each session without the need for prompting (Mulcahy &
Krezmien, 2009).
Deficits in executive functioning, including the ability to think through and
control behavior, also affect students with EBD (Barkley, 2006). Executive functioning
skills include planning, organizing, mentally representing a task, tolerance response, task
visualization, switching strategies, and self-regulation (Hummer et al., 2011; Mueller &
Tomblin, 2012). Because difficulties in self-regulation impact behavior control, social
interactions, and academic performance, schools should implement behavioral
interventions that help students with EBD monitor, adapt, and generalize their own
behavior across multiple settings (Barkley, 2006).
Self-Monitoring
Research suggests that teachers of students with EBD spend only 30% of the
school day on academic instruction while devoting a majority of the time to managing
undesired behaviors (Wehby, Lane, & Falk, 2003). Improving academic outcomes
requires teachers to understand the behavioral process and integrate behavioral
interventions that shift behavior management from teacher to student. An important goal
17
of any educational system is to provide students with the skills needed to become self-
sufficient learners who can manage their own behavior as a means of achieving academic
success (Rafferty, 2010). Therefore, students must learn to manipulate the antecedents
and consequences of their own behavior as they use self-monitoring techniques to make
target behaviors more or less likely (Skinner, 1953). Although many students master this
goal with little direct instruction, other students may struggle to meet academic and
behavioral demands (Fallon, O’Keefe, Gage, & Sugai, 2015). These struggles may be
more pronounced for students from a low socioeconomic status or a minority population
(Fallon et al., 2015).
Self-monitoring is a self-management strategy that can benefit students with and
without disabilities (Graham-Day, Gardner, & Hsin, 2010; Mathur, Griller Clark,
LaCroix, & Short, 2017; Wills & Mason, 2014). Self-monitoring is the process of
observing and self-recording one’s own behavior in order to increase more socially
appropriate behaviors and generalize those behaviors across multiple settings (Stahr,
Cushing, Lane, & Fox, 2006; Trevino-Maack, Kamp, & Wills, 2015). Self-monitoring
interventions have been associated with increases in various desired behaviors, including
on-task behavior, academic performance, task completion, and social behaviors (Amato-
Zech, Hoff, & Doepke, 2006; Holifield, Goodman, Hazelkorn, & Heflin, 2010; Legge,
DeBar, & Alber-Morgan, 2010; Wills & Mason, 2014).
Self-monitoring interventions vary in design and application. Some self-
monitoring interventions involve prerecorded tones emitted through audio devices
(Graham-Day et al., 2010; Harris, Friedlander, Saddler, Frizzelle, & Graham, 2005).
18
Others involve a check-in technique where the student self-assesses and records his or her
own behavior using daily behavior cards (Lane, Capizzi, Fisher, & Ennis, 2012; Miller,
Dufrene, Olmi, Tingstrom, & Filce, 2015). Other self-monitoring interventions require
completion of a checklist or yes/no chart to self-monitor behavior (Holifield et al., 2010;
Parker & Kamps, 2011). Some interventions combine technology with pencil-and-paper
reporting systems (Amato-Zech et al., 2006; Legg et al., 2010).
Self-Monitoring to Increase On-Task Behavior
Much of the existing self-monitoring research focuses on students in a traditional
school setting (e.g., public school, elementary level, general education classroom). The
use of behavior checklists, student behavior logs, and self-graphing techniques have been
associated with increased time on-task for students with challenging behaviors at the
elementary (Amato-Zech et al., 2006; Harris et al., 2005; Reid, Trout, & Schartz, 2005;
Rock & Thread, 2007; Stahr et al., 2006; Stotz, Itoi, Konrad, & Alber-Morgan, 2008),
middle school (e.g., Gureasko-Moore, DuPaul, & White, 2007; Sutherland & Snyder,
2007), and high school (Graham-Day et al., 2010; Legge et al., 2010) levels. Since many
students with challenging behaviors are likely to be placed in alternative schools or
juvenile justice facilities, research on self-monitoring in these settings could support
evidence for its use with a wider population.
Self-monitoring with reinforcement. Self-monitoring plus reinforcement (e.g.,
tokens, candy, praise) has been shown to impact on-task behavior. Multiple studies
investigating self-monitoring interventions with reinforcement rewarded participants if
their recorded data matched the researchers’ (e.g., Freeman & Dexter-Mazza, 2004;
19
Graham-Day et al., 2010), classroom teachers’ (e.g., Wolfe, Heron, & Goddard, 2000), or
peers’ recordings (e.g., Mitchem, Young, West, & Benyo, 2001). Combining accuracy-
and goal-based contingencies is another effective reinforcement technique. For example,
Barry and Messer (2003) used an ABABAB design to assess the effects of self-
monitoring with reinforcement across five sixth-grade students with attention deficit
hyperactivity disorder (ADHD). Target behaviors included on-task behavior, academic
performance, and disruptive behavior. Students created a menu of desired motivators as
a means of reinforcement for reaching pre-determined behavioral goals. Additional
reinforcements were awarded if the student was 100% accurate in response recordings
when compared to the teacher’s data. Across all five participants, on-task behavior and
academic performance were higher during intervention phases than in comparison
phases. Additionally, intervals of disruptive behaviors were lower for each participant
during intervention phases than in comparison phases (Barry & Messer, 2003).
Davis et al. (2014) conducted a similar study in which they provided participants
with reinforcement for both recording accuracy and meeting pre-determined behavioral
goals. During the course of the study, a 15-year-old male with no diagnosed disability
used a VibralLITE 3™ wristwatch to monitor his on-task behavior. The participant had a
chance to earn two tokens during each observation period. The participant earned one
token for meeting a behavioral goal and earned a second token if his recorded data and
that of the observer reached an interobserver reliability rating of at least 80%. After
earning five tokens, the participant could obtain a gift card to a local coffee shop. The
participant’s on-task behavior increased from 62% of observed intervals at baseline to
20
69% during self-monitoring alone. His on-task behavior increased to 91% of observed
intervals when the self-monitoring with reinforcement was implemented (Davis et al.,
2014).
Mitchem et al. (2001) also conducted a study using reinforcement in conjunction
with self-monitoring. The researchers utilized a class-wide peer-assisted self-
management program (CWPASM) across three seventh-grade, general education classes.
Target behaviors included on-task behavior and the use of social skills (i.e., following
instructions and gaining teacher attention). The CWPASM intervention focused on the
blending of two concepts: teams of peer partners who earned and reported points and
self-management procedures. After random pair assignment, students would rate their
behavior and that of their partner according to the provided rating scale. If the ratings
matched, each pair received points for the team. At the end of each day, the pair with the
most points was declared the winner. If each team’s points exceed points from the
previous day, both teams received reinforcement. During CWPASM, the average
percentage of on-task behavior across all three classes increased from .7% during
baseline to 68.9% during the intervention phase. The on-task behavior of 10 target
students increased from approximately 35% of intervals observed during baseline to 80%
with the intervention. In addition to an increase in on-task behavior, the use of selected
social skills increased across target students (Mitchem et al., 2001).
Self-monitoring and function-based support. Functional behavioral assessment
(FBA) is used to determine the function (i.e., purpose) of a target behavior (e.g., escape
aversive stimulus, obtain pleasant stimulus). An FBA allows a behavior support team to
21
effectively design and implement interventions related to the function of the problem
behavior (Hansen, Wills, Kamps, & Greenwood, 2014). Multiple studies have
incorporated FBAs as a way to (a) identify the function of the target behavior (e.g., off-
task behavior) and (b) design function-based interventions (e.g., Briere & Simonsen,
2011; Germer et al., 2011; Lo & Cartledge, 2006; Smith & Sugai, 2000; Stahr et al.,
2006). Stahr et al. (2006) examined the effects of a multicomponent, function-based
intervention packet (i.e., color-coded card communication system, self-monitoring, and
extinction) on the behavior of a 9-year-old male in an alternative educational placement
for students with emotional and behavioral problems. The results of the initial FBA
provided the behavioral support team with data indicating that the participant’s off-task
behavior served the dual functions of (a) escape from an undesired task and (b) obtaining
teacher/adult attention. After implementing the intervention, the participant’s on-task
behavior increased, on average, from 34% to 71% of observed intervals during his
language arts class and from 12% to 64% during math instruction (Stahr et al., 2006).
Using an ABAB withdrawal design, Majeika et al. (2011) found similar results
when analyzing the relationship between a functional-assessment based intervention
(FABI) and on-task behavior on a 17-year-old high school student receiving special
education services under other health impairment for ADHD. The intervention involved
three components. The first component focused on adjusting antecedents (i.e.,
developing a behavior contract, implementing a self-monitoring checklist, demonstrating
how to appropriately access teacher attention, having the student in the proper seat, and
increasing circulation around the room). The second component, adjusting the
22
reinforcement, incorporated increased use of positive behavior supports techniques (i.e.,
behavior specific positive praise, daily and weekly rewards contingent on appropriate
behavior, and PBIS tickets). The final component, extinction, included ignoring off-task
behavior while increasing praise for desired behavior (i.e., withholding attention for off-
task behavior, providing specific praise to others for appropriate behavior, and using brief
if/then redirection). Data showed that on-task behavior averaged 53% of intervals
observed during the baseline phase, 80% during the intervention phase, 48% during the
withdrawal phase, 83% during reintroduction of the intervention, and 70% during the
maintenance phase. Results indicated a functional relationship between the FABI and
on-task behavior (Majeika et al., 2011).
Self-monitoring with technology. The integration of technology in self-
monitoring interventions has proven effective as (a) a prompting device to record
behavior and (b) a medium for recording behavioral data. Prompting devices include
audiotape players (e.g., Freeman & Dexter-Mazza, 2004), cellphones (e.g., Bedesem,
2012), electronic tablets (e.g., Blood et al., 2011), personal digital assistants (PDA; e.g.,
Gulchak, 2008), and a MotivAider™ (e.g., Briesch & Daniels, 2013). Researchers have
also used technology as both a prompting device and a medium for recording data
(Bedesem, 2012; Gulchak, 2008; Wills & Mason, 2014).
Gulchak (2008) used a PDA (i.e., Palm Zire 72) as a self-monitoring device to
prompt and record on-task behavior in 10 min intervals during a 1-hr reading period.
Using an auditory alarm notification, the PDA prompted the sole participant, an 8-year-
old male diagnosed with EBD, to check the on-task box on the PDA as “yes” or “no”
23
every 10 min. At the end of the period, the participant would run a summary report and
graph behavioral progress by manually typing the data into a spreadsheet. After the
implementation of the self-monitoring interventions, on-task behavior increased from
64% of observed intervals to 98% (Gulchak, 2008).
Using more current technology, Bedesem (2012) studied the effect of an
intervention called “CellF-monitoring” on the on-task behavior of two middle school
students receiving inclusion services in a general education language arts classroom.
Self-monitoring prompts were sent via text messages to cellphones four times at 5 min
intervals during the observation period. The text messages prompted students to answer,
“Are you on task?” by typing “1” for “Yes” and “0” for “No”. Data indicated that on-
task behavior increased, on average, from 45% of observed intervals to 71% during the
CellF-monitoring intervention phase (Bedesem, 2012).
Similarly, Wills and Mason (2014) investigated the impact of tablet-based
prompts on the on-task behavior on two high school students receiving special education
services in a general education classroom. The I-Connect application on a Samsung
Galaxy Player 5.0 tablet prompted participants to self-monitor at 5 min fixed intervals
during the observation period. Visual cues (i.e., flashing screen) prompted students to
answer, “Are you on task?” with a yes/no option displayed on the tablet. Results
indicated that on-task behavior increased, on average, from 35% of intervals observed to
92% during the intervention phase (Wills & Mason, 2014).
24
Self-Monitoring to Increase Academic Performance
Although research supports the use of self-monitoring as an effective method for
increasing desired behaviors, results are mixed when measuring the effects of self-
monitoring on academic performance. For example, Lannie and Martens (2008) used a
multiple baseline design across participants to determine the effects of self-monitoring on
on-task behavior, academic productivity, and academic accuracy during math instruction.
Study participants were four elementary students diagnosed with specific learning
disabilities. All participants had a documented history of off-task behavior and exhibited
difficulties in the area of math fluency. After implementing self-monitoring strategies,
on-task behavior increased across all participants; however, participants exhibited
differential effects with accuracy and productivity (Lannie & Martens, 2008).
Similar to Lannie and Martens (2008), Wolfe et al. (2000) examined the effects of
self-monitoring on a target behavior and academic performance. Wolfe et al. used a
reversal ABABABC experimental design with changing criterion to examine the effects
of self-monitoring on on-task behavior and written language performance of four 9-year-
old students receiving special education services in a resource room. Although results
indicated a functional relationship between self-monitoring and on-task behavior, no
functional relationship was determined between the intervention and written language
performance (Wolfe et al., 2000).
Caldwell and Joseph (2012) also examined the effects of a self-monitoring
intervention on on-task behavior, academic productivity, and academic accuracy during
math instruction. This study utilized a reversal ABABC design across participants. The
25
participants were three high school females housed in a maximum-security juvenile
facility. At the time of the study, all participants received special education services and
had a documented history of behavioral problems (e.g., off-task). Results indicated that
on-task behavior increased minimally as a result of the self-monitoring intervention
(Caldwell & Joseph, 2012). Similar to results determined by Lannie and Martens (2008),
there was little evidence to support a functional relationship between self-monitoring and
academic productivity and accuracy.
In a similar study, Lively et al. (2019) used a randomized multiple-baseline design
across participants to examine the effects of a self-monitoring intervention on on-task
behavior on three male students in a secure juvenile facility. At the time of the pilot
study, two of the three students received special education services. The self-monitoring
intervention took place in a high school English class and consisted of a student
worksheet to track behavior, a vibrating watch to remind students to record their behavior
every 5 minutes, and one-on-one training on how to use the intervention tools. Results
indicated that the self-monitoring intervention was associated with an increase in on-task
behavior across all three students (Lively et al., 2019).
Rationale for the Current Study
Research indicates that the implementation of self-monitoring both alone and with
additional components (e.g., reinforcement) can be an effective intervention for
increasing on-task behavior for students with challenging and problematic behaviors
across grade levels (i.e., elementary, middle, and high school). Results from studies of
interventions combining self-monitoring with reinforcement also indicated increased on-
26
task behavior for participants; however, results did not indicate a functional relationship
between self-monitoring (alone or with reinforcement) and an increase in academic
productivity, accuracy, and achievement.
While EBD is prevalent among students in juvenile justice facilities, little
research exists on the use of school-based self-monitoring with this population (Barry &
Gaines, 2008). Much of the research examining the use of self-monitoring techniques in
the juvenile justice system involve trauma-based care interventions (Ford & Blaustein,
2013; Havens, Ford, Grasso, & Marr, 2012; Steiner et al., 2011).
As previously mentioned, both students with EBD and those involved in the
juvenile justice system commonly exhibit difficulties with self-regulation (Barkley, 2006;
Hummer et al., 2011; Moilanen, 2007; Mueller & Tomblin, 2012). Students who are
unable to effectively master self-regulation are at a greater risk of engaging in antisocial
and criminal behavior (Cauffman & Steinberg, 2000; Vitacco, Neumann, Robertson, &
Durrant, 2002). Fine et al. (2018) examined the effects of students’ attitudes on self-
regulation and recidivism rates in the Florida Juvenile Justice System. Results indicated
that students with higher levels of self-regulation were less likely to recidivate than
students with lower levels of self-regulation. However, it should be noted that the effect
of self-regulation diminished as criminal attitudes increased (Fine et al., 2018).
Self-regulation skills allow students to be more actively engaged in the
educational process. Given the academic and behavioral needs of incarcerated juveniles,
instruction in self-regulation is essential. There continues to be a need to further
investigate the effectiveness of self-monitoring on desired behavior of students involved
27
in the juvenile justice system. The purpose of this replication study was to investigate the
effects of a self-monitoring intervention on on-task behavior of students who are
incarcerated in a secure justice facility. This study contributes to the current literature
base on effective practices for educating students in the juvenile justice system, the
effectiveness of self-monitoring, and the ability of teachers to implement an efficient
intervention with students with a wide range of behaviors and disabilities in a secure
juvenile setting.
CHAPTER III
METHODOLOGY
The current replication study used a concurrent baseline design across participants
with randomized intervention start points and order of intervention to determine if there
was a functional relationship between the self-monitoring intervention and an increase in
on-task behavior. This chapter describes the methods for implementing this study.
Specifically, this chapter provides a description on the setting, participants, independent
variable, dependent variable, interobserver agreement, social validity measures, study
design, procedures, fidelity of implementation, and data analysis.
Setting
Prior to beginning any research and recruitment, I obtained all necessary
permissions from the institutional review board and the facility where the research was
conducted. The study took place in a secure juvenile facility in Texas with an average
daily population of 116 male students in grades nine through twelve. At the time of the
study, the school’s population was 40% African American, 29% Hispanic, 20%
Caucasian, <1% Native American, and <1% other. Approximately 8% of the population
was Limited English Proficient and 31% of the population received special education
services. Students with EBD accounted for approximately 60% of the campus special
education population. Students housed in the secure facility were between 15 and 18.11
years of age.
The secure juvenile facility offered educational, medical, residential, and
therapeutic services to students. In 2016, the facility completed facility-wide PBIS
implementation. The multi-tiered system of support was implemented to increase desired
behaviors and decrease undesired behaviors through the delivery of structured
reinforcement and support. Students attended school five days a week except for state
holidays and predetermined breaks (e.g., professional development days, incentive days,
and annual leave days). The school operated on a year-round, modified A/B block
schedule. Students attended three 69-min long classes per day for a total of 207
instructional minutes per day.
Classes ranged from three to eight students with one certified teacher of record
providing instruction. Enrollment fluctuated due to schedule changes and the result of
students being released and admitted to the facility. Although there were at least two
juvenile correctional officers assigned to each hallway, an officer was not specifically
assigned to the classroom used in the study. To be eligible for the study, students had to
be (a) between 14 and 18.5 years old; (b) have documentation (e.g., teacher reports,
discipline referrals, academic records) indicating a history of inattention, off-task
behavior, distraction, and low-level disruptive behavior during academic classes; and (c)
have a math class with the teacher designated for the study.
The math teacher’s classroom was chosen for this study because of the (a) number
of students seen by the math teacher each day and (b) the willingness of the math teacher
to serve as the interventionist. He had been a math teacher at the facility for five years
and was certified in mathematics (Grades 8-12). Students in his classroom were seated at
30
individual desks arranged in four rows with three desks per row. The teacher provided
group instruction at the front of the room using an interactive board. While students
worked individually, the teacher circulated the room.
The math teacher reviewed the eligibility criteria (described above) and created a
list of students who met that criteria based on his own observations, office discipline
referral data, and daily behavior reports. Once students were identified by the teacher, I
gained assent from each student. Using a verbal script, I explained the study to each
student privately (i.e., one-on-one) before math class and included the expectations for
their participation, the purpose of the study, and a clear statement about the student’s
ability to withdraw from the study at any time without having to state a reason. After I
obtained student assent, the education clerk at the school sent a parental consent form to
the parents/guardians of potential participants. The letter was mailed (via US mail) in an
envelope containing (a) the consent form and (b) a self-addressed stamped envelope for
the parents to return the form if they gave permission for their child to participate.
Parental consent forms were available in Spanish, if records indicated Spanish was the
parents’ preferred language of communication. One student identified by the teacher was
over the age of 18. The education clerk at the school sent an adult student consent form
to the student’s housing unit located at the facility. The letter was in an envelope that
contained (a) the consent form and (b) an interdepartmental envelope for the student to
return the form if giving consent.
31
Participants
Three of the 12 students nominated by the teachers were selected to participate in
the study. See Table 1 for demographics for each participant.
Table 1
Demographic Characteristics of Participants
Participant Age Race Grade Disability
LEP
Status
Months in
Facility
1 18 B 11 ID/ED No 5
2 17 H 11 ADHD Yes 6
3 16 W 10 ED No 6
Note. Race. B = Black, H = Hispanic, W = White. Disability. ADHD = Attention Deficit Hyperactivity
Disorder, ED = Emotional Disturbance, ID = Intellectual Disability
Student 1
Student 1 was an 18-year-old Black male classified as an eleventh grader
receiving special education; he was diagnosed with both an intellectual disability and an
emotional disturbance. According to his Individualized Education Program (IEP), he had
academic and behavioral accommodations, a modified curriculum, and inclusion support
in his math course. He had been incarcerated at the facility for 5 months at the time the
study began and had a documented history of inattention, off-task behavior, and low-level
disruptive behavior. Since arriving at the facility, he had received 25 behavior reports
(i.e., the facility’s version of office discipline referrals); 18 occurred in the math
classroom used in the study. He received seven incident reports (i.e., four major rule
violations and three minor rule violations). Of the seven incidents, one minor rule
32
violation (i.e., “horse playing”) took place in the math classroom. Based on his grade
report history, his average in math at the time the study began was 72%. The Test of
Adult Basic Education (TABE) is a diagnostic tool used to assess an individual’s grasp of
the basic skills needed to be successful in both the academic arena and workplace. TABE
assessments are administered and scored using computer software. Scores range from the
0.0 grade-level to the 12.9 grade-level. According to TABE results, he scored at the 2.1
grade-level in reading and the 1.6 in math. See Table 2 for behavioral and academic
characteristics of all participants.
Student 2
Student 2 was a 17-year-old Hispanic male classified as an eleventh grader
receiving special education; he was diagnosed with ADHD and was eligible for special
education under the “other health impairment” category. According to his IEP, he
received academic and behavioral accommodations in his math course. He was also
classified as Limited English Proficient and participated in the school English as a
Second Language program. He had been incarcerated at the facility for 6 months and had
a documented history of inattention, off-task behavior, and low-level disruptive behavior.
Since arriving at the facility, he received 22 behavior reports; 10 occurred in the math
classroom used in the study. He received three incident reports for minor rule violations.
Of the three incidents, two minor rule violations (i.e., both “undesignated area”) took
place in the math classroom. Based on his grade report history, his average in math at the
time the study began was 75%. According to TABE results, he scored at the 5.6 grade-
level in reading and the 7.9 in math.
33
Student 3
Student 3 was a 16-year-old White male classified as a tenth grader receiving
special education services; he was diagnosed with an emotional disturbance. According
to his IEP, he received academic and behavioral accommodations in his math course. He
had been incarcerated at the facility for 6 months and had a documented history of
inattention, off-task behavior, and low-level disruptive behavior. Since arriving at the
facility, he received 48 behavior reports (similar to office discipline referrals); 29
occurred in the math classroom used in the study. He received 20 incident reports (i.e.,
six major rule violations and 14 minor rule violations). Of the 20 incidents, four minor
rule violations (i.e., “refusal to follow staff instructions, “horse playing”, and “disruption
of scheduled activity”) took place in the math classroom. Based on his grade report
history, his average in math at the time the study began was 62%. According to TABE
results, he scored at the 10.7 grade-level in reading and the 9.9 in math.
Table 2
Academic and Behavioral Characteristics of Participants
Number of
Behavior Reports
Number of
Incident Reports
Participant
TABE
Reading
TABE
Math
Current
Math
Average Total In Math Total In Math
1 2.1 1.6 72 25 18 7 1
2 5.6 7.9 75 22 10 3 2
3 10.7 9.9 62 48 29 20 4
Note. TABE scores correlate with grade-level.
34
Independent Variable
The independent variable in this study was a self-monitoring tool which consisted
of a worksheet to track behavior, a vibrating watch to remind students to record their
behavior (with a reminder set for 5-min intervals), and training in how to use the
worksheet and vibrating watch. The different phases of intervention are described in
detail in the “Procedures” section.
Dependent Variable
The dependent variable was on-task behavior. On-task behavior was defined as a
student doing all of the following: (a) sitting in his seat; (b) looking at work on his desk;
(c) directing his attention to an area or object as indicated by the teacher; (d) remaining
silent unless asking a question or responding to a question; and (e) following classroom
expectations, including keeping hands to himself, raising hand to contribute, and
following all teacher directions within 5 s of being asked. Data were collected during 15-
min direct observations using 10-s whole-interval recording to determine the percentage
of intervals per observation with on-task behavior (see Appendix A). Data collectors
calculated the percentage of intervals with on-task behavior by dividing the total number
of intervals with on-task behavior by the total number of intervals observed (i.e., 90
intervals observed in a 15-m observation) to yield a percentage.
Data collectors
Data collectors were four special education doctoral students. All data collectors
completed both the online Social and Behavioral Research – Basic/Refresher and Social
and Behavioral Responsible Conduct of Research training courses provided through the
35
CITI Program. This was followed by successful completion of the IRB approval process
at Texas Woman’s University and background checks conducted by the secure facility.
Data collectors were trained prior to the study to ensure that could accurately collect data.
Training consisted of (a) an operational definition of on-task behavior, including
examples and non-examples; (b) practice collecting data on on-task behavior using the
10-s whole-interval coding sheet; and (c) practice documenting treatment fidelity using
the fidelity checklist (see Appendix B). I trained data collectors for two hours prior to the
start of the study. Training took place in a natural setting. Data collectors had to
demonstrate 90% or greater inter-observer agreement (IOA) across three consecutive
observations prior to the beginning of the study. IOA was determined by dividing the
number of agreed-upon observations by the total number of intervals observed.
For the 81 direct observations, 27 of them were done with two data collectors to
establish IOA. IOA was calculated for 33% (i.e., 27/81) of the observations. To ensure
the integrity of the reliability checks, checks were scheduled at random throughout the
duration of the study. Overall IOA for the study was 93% (range 76%-100%), calculated
by dividing the total number of intervals with agreement by the total number of intervals
observed and calculating a percentage.
Social Validity
The Self-Monitoring Intervention Social Validity Survey: Student and Teacher
Versions (Lively et al., 2019) were used to collect descriptive data on social validity of
the intervention from the students’ and teacher’s perspectives. At the conclusion of the
study, the students and the math teacher were given a corresponding social validity
36
survey. The five-question surveys used a 5-point Likert scale, with 5 representing
strongly agree and 1 representing strongly disagree, to determine participant satisfaction
with the intervention. The surveys included questions about satisfaction with (a) the ease
of the intervention, (b) whether the person would recommend the intervention to others,
(c) the time commitment of the intervention, (d) the effectiveness of the intervention, and
(e) whether the person would be inclined to use the intervention in the future. A copy of
each questionnaire is included in Appendix C and D.
Procedure
This replication study was a single-subject, concurrent baseline design across
participants with randomized intervention start points and order of intervention.
Randomizing the order participants receive the intervention and the intervention start
points reduces threats to internal validity in single-subject design (Levin, Ferron, &
Gafurov, 2016). After the participants were selected and the requisite consent and assent
obtained, a member of the research team assigned each participant a number. I only had
access to the numbers and the data were only identifiable by number; I had no access to
identifiable information about each participant for this process. Participant numbers were
put into an opaque container, then three numbers were drawn and listed on a whiteboard.
After the order was randomly assigned, all participants were randomly assigned an
intervention start point. It was predetermined that each participant would have at least
five baseline data collection observations. I wrote each number from 6-20 on a piece of
paper, folded the papers, and placed the numbers in an opaque container. Then, I
selected a number for each participant number (in the order which the participants’
37
numbers appeared on the whiteboard). This number determined the data point where the
intervention began. Once the start points were determined, baseline data collection
began. The baseline, intervention, and maintenance phases of the study are described
below.
Baseline
During the baseline phase, trained data collectors completed a minimum of five
15-min observations of each of the three participants using 10-s whole-interval recording
to determine levels of on-task behavior. Observations occurred during the first 15 min of
each participant’s scheduled math class to minimize possible disruption. Data collectors
conducted baseline observations over 60 calendar days (i.e., 15 days for Student 1, 31
days for Student 2, and 60 days for Student 3). Data collection for the entire study
occurred over 88 calendar days.
Intervention
After completing baseline data collection for the first participant as determined
through the random-assignment process described above, a doctoral student conducted a
1-hour training session with the math teacher. Using training methods similar to those
used by Lively et al. (2019), the doctoral student followed a training checklist and
explained the self-monitoring intervention, described the operational definition of on-task
behavior, and provided the teacher copies of all training materials, student self-
monitoring sheets, and a programmable watch set to vibrate at predetermined intervals. I
observed the teacher training and completed a procedural fidelity checklist to ensure all
components were covered in the training. The procedural fidelity checklist provided
38
spaces to check off when the following were provided: (a) an explanation of the self-
monitoring intervention; (b) an operational definition of on-task behavior; and (c) copies
of all training materials, self-monitoring sheets, and tools needed (i.e., vibrating watch).
At the conclusion of each participant’s baseline phase, the math teacher scheduled
a 15-min training session with each student participant. The individual training sessions
took place in the math teacher’s classroom and consisted of the math teacher training the
student participant in the self-monitoring intervention using a script developed by me.
During the training, the math teacher demonstrated how to use the self-monitoring sheet
(see Appendix E) and provided examples and non-examples of on-task behavior. The
teacher explained to the student that when he felt the watch vibrate, he would mark a “+”
in the corresponding box on the self-monitoring sheet if he was on task and a “-” if he
was not on task based on the previously provided examples and non-examples of on-task
behavior. I observed the student training sessions and completed a procedural fidelity
checklist to ensure all components were covered in the training. The procedural fidelity
checklist provided spaces to check off when the following were provided: (a) an
explanation of how to use the self-monitoring sheet; (b) a demonstration of how to use
the vibrating watch; and (c) an operational definition of on-task behavior including
examples and non-examples of on-task behavior.
On the first day of each student’s intervention phase, the math teacher provided
the student with the self-monitoring sheet and the vibrating watch at the beginning of the
class and provided a verbal reminder that the student would be prompted by the watch
every 5 min and should mark a “+” or “-” to identify whether or not he was on-task at
39
that moment. At the conclusion of the class period, the math teacher collected the
vibrating watch and the student self-monitoring sheets and filed the sheets in the
student’s work folders. These sheets were not used as data for the study. Although direct
observation occurred for the only the first 15 min of the class, students were expected to
self-monitor for the 69-min class period. There was no additional reinforcement or
prompting to accompany the self-monitoring intervention in order to investigate if self-
monitoring alone (i.e., increasing awareness of one’s own behavior) increased the
likelihood of on-task behavior. During the intervention phase, data collectors observed
each student participant until at least six data points had been collected or until the data
appeared to be stable, at which point the intervention was discontinued.
Maintenance
During the maintenance phase, students no longer had access to the vibrating
watch. The student could still request the self-monitoring sheet from the teacher when he
picked up his materials at the beginning of the math class. If this occurred, the math
teacher provided the student with the self-monitoring sheet. As in the intervention phase,
the teacher collected the self-monitoring sheet at the end of class and filed it in the
student’s work folders. During maintenance, students received no further reinforcement
or prompting from the math teacher to self-monitor.
Fidelity
Prior to the start of the study, I conducted an individual 30 min training session
with a doctoral student. During the training, I (a) explained the self-monitoring
intervention; (b) described the operational definition of on-task behavior (including
40
examples and non-examples); (c) provided the doctoral student copies of all training
materials, student self-monitoring sheets, and a programmable watch, and (d)
demonstrated how to complete the teacher training. The trained doctoral student
followed a training checklist to train the math teacher. I observed the teacher training and
completed a procedural fidelity checklist to ensure that the trainer (a) explained the self-
monitoring intervention; (b) described the operational definition of on-task behavior
(including examples and non-examples); and (c) provided the teacher copies of all
training materials, student self-monitoring sheets, and a programmable watch set to
vibrate at predetermined intervals.
Prior to beginning each student’s intervention phase, the math teacher scheduled a
15-min scripted training session with each student. During the individual training
sessions, the math teacher described on-task behavior (including examples and non-
examples) and demonstrated how to use the self-monitoring sheet. Specifically, the
teacher explained to the student that when he felt the watch vibrate (5-min intervals), he
would mark a “+” on the self-monitoring sheet if he was on task and a “-” if not on task
based on the previously provided examples and non-examples of on-task behavior. I
observed each individual training sessions and completed a procedural fidelity checklist
to ensure these steps were covered in the training.
During each observation in the intervention and maintenance phases, data
collectors completed a fidelity check. The fidelity checklist included two components.
The first component corresponded with the intervention phase. During direct
observation, data collectors recorded whether the student completed the task “fully” (i.e.,
41
recorded behavior on SM sheet 100% of the time when prompted), “partially” (i.e., did
not SM 100% of the time but did more than 0% of the time), or “not at all” (i.e., did not
obtain the watch or did not SM when prompted). The second fidelity checklist
component corresponded to the maintenance phase. During direct observation, data
collectors recorded whether the student completed the task “fully” (i.e., obtained the SM
sheet from the teacher and recorded behavior at least once), “partially” (i.e., obtained the
SM sheet from the teacher but did not record behavior), or “not at all” (did not obtain the
SM sheet from the teacher). Of the 81 direct observations, 48 occurred during the
intervention and maintenance phases. For the 48 direct observations taking place after
baseline, data collectors completed the fidelity check 100% (i.e., 48/48) of the
observations.
Data analysis
The data were visually analyzed to determine whether there were between-phase
changes in each student’s response level. Specifically, visual analysis was used to assess
whether increases in students’ levels of on-task behavior occurred simultaneously with
the staggered introduction of the self-monitoring intervention. If the introduction of the
intervention coincided with the students’ levels of on-task behavior, visual analysis was
used to determine whether those increases continued throughout the intervention and
maintenance phased.
CHAPTER IV
RESULTS
The current replication study was designed to determine if there was a functional
relationship between self-monitoring and an increase in on-task behavior in three male
students in a secure juvenile facility. During each observation, trained data collectors
used 10-s whole-interval recording to track the student’s on-task behavior. In addition,
data collectors completed an intervention fidelity checklist during each intervention and
maintenance phase observation. Three students participated in the replication study. The
following section will discuss the results of all three students, beginning with an
overview of the data and then a detailed examination of the results for each individual
student.
Overview of the results
Figure 1 provides a graphic representation of the percentage of intervals with on-
task behavior data for each of the three students. Visual analysis of Figure 1 indicated a
functional relationship between the intervention of self-monitoring and student on-task
behavior. The introduction of the self-monitoring intervention was followed by an
immediate and sustained increase in student on-task behavior.
Student 1
Student 1 had five baseline observations and averaged 37% of intervals with on-
task behavior (range 27-46%). During the intervention phase, he was observed six times
and averaged 79% of intervals with on-task behavior (range 70-91%). Student 1 had 16
maintenance observations, during which he averaged 63% of intervals with on-task
behavior (range 57-77%). Visual analysis indicated an immediate and clear separation of
level between baseline phase and the intervention phase with no overlapping data points
between the two phases.
His behavior reports (i.e., the facility’s version of office discipline referrals)
decreased from 25 to 7 overall and from 18 to 0 in the math classroom. His incident
reports decreased from seven to zero. His incident reports in the math classroom
decreased from one to zero. Based on his grade report history, his academic average in
math increased from 72% prior the intervention to 81% at the conclusion of the study.
According to TABE results, his score in reading increased from the 2.1 grade-level to the
3.2 grade-level. Similarly, his score in math increased from 1.6 to 4.5. See Table 3 for
behavioral and academic results of all participants.
Student 2
Student 2 had 11 baseline observations and averaged 45% of intervals with on-
task behavior (range 29-78%). During the intervention phase, he was observed six times
and averaged 87% of intervals with on-task behavior (range 76-100%). Student 2 had 10
maintenance observations, during which he averaged 81% of intervals with on-task
behavior (range 78-86%). Visual analysis indicated an immediate and clear separation of
level between baseline phase and the intervention phase with one datum point
(observation 1) that overlapped with the baseline phase (observation 15). It should be
44
noted that during observation 1, an inclusion teacher provided one-on-one support to
Student 2 for 11 minutes of the 15-min observation.
His behavior reports decreased from 22 to 13 overall and from 10 to 0 in the math
classroom. His incident reports decreased from three to one. His incident reports in the
math classroom decreased from two to zero. Based on his grade report history, his
academic average in math increased from 75% prior the intervention to 86% at the
conclusion of the study. His TABE score in reading increased from the 5.6 grade-level
to the 6.9 grade-level. Similarly, his score in math increased from 7.9 to 9.9.
Student 3
Student 3 had 17 baseline observations and averaged 39% of intervals with on-
task behavior (range 29-50%). During the intervention phase, he was observed six times
and averaged 77% of intervals with on-task behavior (range 69-81%). Student 3 had four
maintenance observations, during which he averaged 70% of intervals with on-task
behavior (range 68-72%). Visual analysis indicated an immediate and clear separation of
level between baseline phase and the intervention phase with no overlapping data points
between the two phases.
His behavior reports decreased from 48 to 27 overall and from 29 to 0 in the math
classroom. His incident reports decreased from 20 to 12. His incident reports in the math
classroom decreased from four to zero. Based on his grade report history, his academic
average in math increased from 62% prior the intervention to 78% at the conclusion of
the study. At the conclusion of the study, Student 3 had not retaken the TABE
assessment; therefore, updated TABE results were not available.
45
Figure 1. Participants’ Percentage of Intervals with On-Task Behavior
46
Table 3
Academic and Behavioral Results of Participants
Number of
Behavior Reports
Number of
Incident Reports
Participant
TABE
Reading
TABE
Math
Current
Math
Average Total In Math Total In Math
1 3.2 4.5 81 7 0 0 0
2 6.9 9.9 86 13 0 1 0
3 10.7 9.9 78 27 0 12 0
Note. TABE scores correlate with grade-level. Student 3 did not have updated TABE results.
Intervention Fidelity
I observed all teacher and student training sessions. A doctoral student conducted
a 1-hour training session with the math teacher. During the training session, the doctoral
student followed a training checklist and explained the self-monitoring intervention,
described the operational definition of on-task behavior, and provided the teacher copies
of all training materials, student self-monitoring sheets, and a programmable watch set to
vibrate at predetermined intervals. I observed the teacher training and completed a
procedural fidelity checklist. The checklist provided spaces to check off when the
following occurred: (a) an explanation of the self-monitoring intervention, (b) an
operational definition of on-task behavior, and (c) provided the math teacher with copies
of all training materials, self-monitoring sheets, and tools needed (i.e., vibrating watch).
Fidelity was 100% for the teacher training session.
47
Prior to the start of each student’s intervention phase, the math teacher scheduled
a 15-min scripted training session with each student. During the individual training
sessions, the math teacher described on-task behavior (including examples and non-
examples) and demonstrated how to use the self-monitoring sheet. I observed each
individual training sessions and completed a procedural fidelity checklist to ensure all
components were covered in the training. Fidelity was 100% for all three student training
sessions.
Treatment Integrity
During the intervention and maintenance phases, data collectors completed a
fidelity checklist during each observation to determine whether the student completes the
self-monitoring intervention “fully,” “partially,” or “not at all.” Of the 81 direct
observations, 48 occurred during the intervention and maintenance phases. For the 48
direct observations taking place after baseline, data collectors completed the fidelity
check 100% (i.e., 48/48) of the observations. Student 1 used the interventions “fully”
100% of the time observed (i.e., six intervention observations and 16 maintenance
observations). Student 2 used the interventions “fully” 100% of the time observed (i.e.,
six intervention observations and 10 maintenance observations). Student 3 used the
interventions “fully” 100% of the time observed (i.e., six intervention observations and
four maintenance observations).
Social Validity
To evaluate the efficiency and effectiveness of using self-monitoring to increase
on-task behavior, each student participant was asked to complete the Self-Monitoring
48
Intervention Social Validity Survey: Student Version (see Appendix C). Students were
asked to complete the survey at the conclusion of the maintenance phase. The five-
question survey used a 5-point Likert scale, with 5 representing strongly agree and 1
representing strongly disagree, to determine participant satisfaction with the intervention.
The results are listed in Table 4.
Table 4
Social Validity Rating by Participants
Survey Item Mean Range
The self-monitoring intervention was easy to use. 4.7 4-5
I would recommend self-monitoring to other students. 4.7 4-5
Self-monitoring did not take up too much of my time. 4.7 4-5
Self-monitoring was effective at improving my behavior. 5.0 5
I will use self-monitoring in the future. 5.0 5
All three students chose 4 (agree) or 5 (strongly agree) for the first three items
indicating that (1) the self-monitoring intervention was easy to use, (2) they would
recommend it to other students, and (3) that it did not take up too much time. All three
students chose 5 (strongly agree) to the final two questions indicating that self-
monitoring was effective at improving behavior and that they would use it in the future.
The math teacher was asked to complete the Self-Monitoring Intervention Social
Validity Survey: Teacher Version (see Appendix D). The math teacher was asked to
49
complete the survey at the conclusion of the maintenance phase. The five-question
survey used a 5-point Likert scale, with 5 representing strongly agree and 1 representing
strongly disagree, to determine the teacher’s satisfaction with the intervention. The
teacher chose 5 (strongly agree) for all survey questions: the self-monitoring intervention
was easy to use, self-monitoring recommended to other teachers, self-monitoring did not
take up too much time, self-monitoring was effective at improving student behavior, and
self-monitoring will be used in the classroom in the future.
CHAPTER V
DISCUSSION
Self-monitoring interventions have been effective at increasing desired behaviors
in both students in the general education setting and those with EBD (Bruhn et al., 2014;
Wexler et al., 2014). Although EBD is prevalent among students in the juvenile justice
system, limited research exists on the use of self-monitoring to increase desired behaviors
in the school setting (Barry & Gaines, 2008). The purpose of this study was to determine
if there was a functional relationship between self-monitoring and an increase in on-task
behavior in three males in a secure juvenile facility. This study was a systematic
replication of the Lively et al. (2019) pilot study that investigated the impact of self-
monitoring on the on-task behavior of adjudicated juvenile males in an English class.
For all three students, the introduction of self-monitoring resulted in an immediate
and sustained increase in on-task behavior. When the vibrating watch was removed (i.e.,
maintenance phase), all three students continued to maintain on-task behavior at rates
above baseline. Additionally, all three students saw a decrease in behavior and incident
reports and an increase in their academic average in math.
Limitations
The current study does have limitations that should be considered when
evaluating and interpreting the findings. First, one limitation of single-case research is
the limited potential for generalization (i.e., external validity). There were no
generalization probes in other classrooms in the facility. It is unclear whether improved
on-task behavior as a result of the self-monitoring intervention would generalize to the
students’ other academic classes or other students in different juvenile justice facilities or
alternative settings. Because the study was conducted in a math classroom with a range
of three to eight students, the extent to which similar results would generalize to other
settings containing a larger range of students is unknown.
Additionally, all observations were conducted in a school setting within a secure
juvenile facility, and certain environmental factors could not be controlled (e.g.,
interruptions, specialized services, and student behavior). For example, during one
observation, an inclusion teacher provided one-on-one support to a student participant.
On four occasions the math teacher was absent, and a substitute teacher covered the class.
Implications
The findings of this study support application of self-monitoring interventions on
increasing on-task behavior on students in a secure juvenile facility.
Implications for Researchers
External validity is strengthened when researchers replicate the experimental
procedures of another study and obtain similar results (Richards, Taylor, & Ramasamy,
2014). One important aim of this study was to strengthen external validity through
replication of Lively et al. (2019). Similar results were obtained in both studies. Results
of this replication study indicated a functional relationship between the self-monitoring
intervention and an increase in on-task behavior in incarcerated male students in a math
classroom. Literature documents the effectiveness of self-monitoring interventions
52
across diverse groups; however, limited research exists on self-monitoring interventions
in secure juvenile facilities (Caldwell & Joseph, 2012; Lively et al., 2019). Replicating
this study would be an important next step to determine if similar results can occur with
additional dependent variables. Although this study sought to increase on-task behavior,
it should be noted that all three student participants increased their academic average in
math. Future studies should explore the use of a self-monitoring intervention to increase
both on-task behavior and academic performance. Researchers could also expand on the
findings of the current study by modifying elements of the independent variable (e.g.,
changing the watch to a timer).
Another implication for researchers is the need for replication across settings.
Lively et al. (2019) examined the use of a self-monitoring intervention in an English
classroom while this replication study took place in a math classroom. Future studies
should explore the effectiveness of self-monitoring across settings to see if students in the
juvenile justice setting can generalize behavior across multiple classrooms.
A final implication for researchers addresses the need for additional maintenance
probes. While this study included maintenance data, it is difficult to determine long-
range effects of the self-monitoring intervention on on-task behavior. Future studies
should examine longitudinal data to determine the effectiveness of the intervention across
participants.
Implications for Practitioners
Finding simple interventions is critical given the demands on teachers, especially
for those who address high rates of problematic behaviors (e.g., teachers in juvenile
53
justice settings). Results from the social validity questionnaire indicated that the self-
monitoring intervention was an efficient intervention that was easy to use and required
limited resources. This study represents how a low-cost, low-resource intervention can
substantially impact student behavior, even students with a history of challenging
behaviors. Additionally, practitioners can easily adapt the self-monitoring intervention to
fit the various needs of a diverse student population. As discussed in Chapter II,
practitioners can add additional components to the self-monitoring interventions
including a reinforcement system, tactical prompts, and technology (e.g., smartphones,
timers, watches).
Summary
In this study, a multiple baseline design across participants was used to
investigate the effects of a self-monitoring intervention on increasing on-task behavior on
male students in a secure juvenile facility. Results from the study strongly suggest a
functional relationship between self-monitoring and an increase in on-task behavior. All
student participants showed immediate and sustained improvements in on-task behavior
when taught to use the self-monitoring intervention. The positive response to the self-
monitoring intervention was consistent with literature. Additionally, all three students
saw a decrease in behavior and incident reports and an increase in their academic average
in math.
Limitations to the current study include its limited potential for generalizability
(i.e., external validity) when based on a limited number of student participants. Factors
including limited participant selection, the facility, the math teacher, the classroom, and
54
other characteristics unique to the juvenile justice setting may reduce generalizability.
Additionally, because there were no generalization probes in other classrooms, it is
unknown whether the self-monitoring intervention increased on-task behavior in other
academic settings.
The current replication study also has several implications for researchers and
practitioners. Future studies should examine the effects of the self-monitoring
intervention on on-task behavior (a) plus additional dependent variables (e.g., academic
performance); (b) across different settings and participates; and (c) using longitudinal
maintenance probes. Practitioners can easily adapt the self-monitoring intervention to fit
the needs of a diverse student population including students with disruptive behaviors.
Given the positive results of this study, there is an apparent benefit for implementation of
self-monitoring intervention in the juvenile justice setting.
55
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75
APPENDIX A
Data Collection Sheet
76
Data Collection Sheet
Student:
Data collector:
Date:
Time (15 min):
Indicate if student is on-task for the entire 10 s interval by marking a “+” in the designated space.
Indicate if student is off-task at any point during the 10 s interval by marking a “-” in the
designated space. Remember, on-task behavior looks like a student doing any or all of the
following: (a) sitting in his seat, (b) facing the teacher if the teacher is speaking, (c) directing his
attention to an area or object as indicated by the teacher, (d) looking at work on his desk, (e)
remaining silent unless asking a question or responding to a question, and (f) following classroom
expectations, including keeping hands to himself, raising hand to contribute, and following all
teacher directions within 5 s of being asked. Non-examples of on-task behavior include sleeping,
talking to peers (unless in teacher-directed group work), looking at the clock or other unrelated
materials for more than 10 s, and talking to himself.
Minute Interval
:10 :20 :30 :40 :50 :60
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Total +
Add up bottom row. What is the total number of intervals with on-task behavior?
_____________
Divide the number above by 90 (i.e., total number of intervals). What is that number? _______
Multiply the number above by 100. What is the percentage of intervals with on-task behavior?
_________
77
APPENDIX B
Fidelity Checklist
78
Fidelity Checklist
Instructions
For this component, record whether the participant complete the task:
a) Fully (recorded behavior on SM sheet 100% of the time when prompted)
b) Partially (did not SM 100% of the time)
c) Not at all (did not obtain the watch or did not SM when prompted)
Component One: Watch plus SM sheet
The participant obtained the watch from the teacher, wore it on his wrist throughout the
entire observation period, and self-monitored each time the watch prompted (i.e.,
vibrated and signaled in writing the words “pay attention”).
Fully ⃞
Partially ⃞ If partially, provide a
brief description.
Not at all ⃞ If not at all, provide a
brief description.
Instructions
For this component, record whether student complete it:
a) Fully (obtained the SM sheet from the teacher and recorded behavior at least
once)
b) Partially (obtained the SM sheet from the teacher but did not record behavior)
c) Not at all (did not obtain the SM sheet from the teacher)
Component Two: SM sheet only (watch was removed)
The participant obtained the SM sheet from the teacher and recorded behavior on the
SM sheet at least once during the observation period.
Fully ⃞
Partially ⃞ If partially, provide a
brief description.
Not at all ⃞ If not at all, provide a
brief description.
79
APPENDIX C
Self-Monitoring Intervention Social Validity Survey: Student Version
80
Self-Monitoring Intervention Social Validity Survey: Student Version
Please fill out the survey below based on the self-monitoring that you were asked to do as a
participant in our recent research study. Indicate whether you strongly disagree, disagree,
neither agree nor disagree, agree, or strongly agree with the statements. Thank you for
your time.
1. The self-monitoring intervention was easy to use.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
2. I would recommend self-monitoring to other students.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
3. Self-monitoring did not take up too much of my time.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
4. Self-monitoring was effective at improving my behavior.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
5. I will use self-monitoring in the future.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
81
APPENDIX D
Self-Monitoring Intervention Social Validity Survey: Teacher Version
82
Self-Monitoring Intervention Social Validity Survey: Teacher Version
Please fill out the survey below based on the self-monitoring that you were asked to do as
a participant in our recent research study. Indicate whether you strongly disagree, disagree,
neither agree nor disagree, agree, or strongly agree with the statements. Thank you for
your time.
1. The self-monitoring intervention was easy to use.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
2. I would recommend self-monitoring to other teachers.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
3. Self-monitoring did not take up too much of my time.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
4. Self-monitoring was effective at improving student behavior.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
5. I will use self-monitoring in my classroom in the future.
1
Strongly
Disagree
2
Disagree
3
Neither agree
nor disagree
4
Agree
5
Strongly
Agree
83
APPENDIX E
Student Self-Monitoring Sheet
84
Student Self-Monitoring Sheet
Name:
Date and time:
Every five minutes (or each time the watch vibrates), indicate whether you are on-task
(+) or off-task (-). Remember, on-task looks like you’re doing your work, paying
attention to the teacher, following directions, and meeting all of the behavior expectations
for the class.
Interval On-task (+) or off-task (-)? Interval On-task (+) or off-task (-)?
1 19
2 20
3 21
4 22
5 23
6 24
7 25
8 26
9 27
10 28
11 29
12 30
13 31
14 32
15 33
16 34
17 35
18 36
