assignment

Complete the charts in the word document (2 tables for each article) 7th APA using the info provides in the articles. 

The topic of our research is   

The task refusal behaviors of a student with special needs by reducing them and increasing the client’s compliance with non-preferred demands and activities in the school.  

At the top, in each table you need include the reference of the article. You have an example included in the word doc.

 

Access 6 full-length articles taken from professional and/or academic journals that publish research findings on the topic (The task refusal behaviors of a student with special needs)

Use the attached worksheets for your responses.

6

Sample Citation in APA 6th edition:

Arbelo, F. (2016). Pre-entry doctoral admission variables and retention at a Hispanic Serving
Institution. International Journal of Doctoral Education, 11, 269 – 284.
http://www.informingscience.org/Publications/3545

Academic Journal Articles:

APA Citation (Refer to APA Writer’s Manual, 6th ed.)

Citation here

Selection

Explanation

Source: Primary or Secondary

Information Classification: (Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)

How and why is this information pertinent to your selected topic?

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)
Citation here

Selection

Explanation

How and why is this information pertinent to your selected topic?

					Issues / Topics Covered
					Author(s):
					Research Question(s) addressed:
					Research Subjects: (pre-K, 9th graders, elementary school students, etc.)
Research setting: (Public school, 3rd grade class, Charter school, adult learning center, etc.)
					Methodology:
					Findings:
					Conclusions:
					Special Circumstances/Limitations:
					Future Implications:

2nd Article

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)

Citation here:

Selection

Explanation

Source: Primary or Secondary

Information Classification:
(Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)

How and why is this information pertinent to your selected topic?

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)

Citation here:

Selection

Explanation

Issues / Topics Covered

Author(s):

Research Question(s) addressed:

Research Subjects: (pre-K, 9th graders, elementary school students, etc.)

Methodology:

Findings:

Conclusions:

Special Circumstances/Limitations:

Future Implications:

How and why is this information pertinent to your selected topic?

				Research setting: (Public school, 3rd grade class, Charter school, adult learning center, etc.)

3rd Article

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)
Citation here:

Selection

Explanation

Source: Primary or Secondary

Information Classification:
(Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)

How and why is this information pertinent to your selected topic?

Academic Journal Articles:

APA Citation (Refer to APA Writer’s Manual, 5th ed.)

Selection

Explanation

Issues / Topics Covered

Author(s):

Research Question(s) addressed:

Research Subjects: (pre-K, 9th graders, elementary school students, etc.)

Research setting:
(Public school, 3rd grade class, Charter school, adult learning center, etc.)

Methodology:

Findings:

Conclusions:

Special Circumstances/Limitations:

Future Implications:

How and why is this information pertinent to your selected topic?

4th Article

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)
Citation here:

Selection

Explanation

Source: Primary or Secondary

Information Classification:
(Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)

How and why is this information pertinent to your selected topic?

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 5th ed.)

Selection

Explanation

Issues / Topics Covered

Author(s):

Research Question(s) addressed:

Research Subjects: (pre-K, 9th graders, elementary school students, etc.)

Research setting:
(Public school, 3rd grade class, Charter school, adult learning center, etc.)

Methodology:

Findings:

Conclusions:

Special Circumstances/Limitations:

Future Implications:

How and why is this information pertinent to your selected topic?

5th Article

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)
Citation here:

Selection

Explanation

Source: Primary or Secondary

Information Classification:
(Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)

How and why is this information pertinent to your selected topic?

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 5th ed.)

Selection

Explanation

Issues / Topics Covered

Author(s):

Research Question(s) addressed:

Research Subjects: (pre-K, 9th graders, elementary school students, etc.)

Research setting:
(Public school, 3rd grade class, Charter school, adult learning center, etc.)

Methodology:

Findings:

Conclusions:

Special Circumstances/Limitations:

Future Implications:

How and why is this information pertinent to your selected topic?

6th Article

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)
Citation here:

Selection

Explanation

Source: Primary or Secondary

Information Classification:
(Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)

How and why is this information pertinent to your selected topic?

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 5th ed.)

Selection

Explanation

Issues / Topics Covered

Author(s):

Research Question(s) addressed:

Research Subjects: (pre-K, 9th graders, elementary school students, etc.)

Research setting:
(Public school, 3rd grade class, Charter school, adult learning center, etc.)

Methodology:

Findings:

Conclusions:

Special Circumstances/Limitations:

Future Implications:

How and why is this information pertinent to your selected topic?

Vol.:(0123456789)

Journal of Behavioral Education
https://doi.org/10.1007/s10864-019-09346-5

1 3

O R I G I N A L PA P E R

Improving Compliance in Primary School Students
with Autism Spectrum Disorder

Tsuyoshi Imasaka1 · Pei Ling Lee1 · Angelika Anderson2  ·
Chernyse W. R. Wong1 · Dennis W. Moore1 · Brett Furlonger1 ·
Margherita Bussaca1

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Abstract
Complying with common instructions is considered an important skill, critical to
school success; however, students with autism spectrum disorder (ASD) often
exhibit low levels of compliance creating barriers to their inclusion in regular gen-
eral education school settings. While self-management interventions have the poten-
tial to address compliance issues, there has been little research investigating their
effectiveness in regular education school settings that include young children with
ASD. Accordingly, the present study examined the effects of a self-management
intervention for two 8-year-old boys with ASD and attention-deficit hyperactivity
disorder. A multiple-baseline across settings design was used. Results indicated that
the intervention was associated with increased rates of compliance and concomitant
increases in on-task behavior for both participants within their respective classroom.
Effects were maintained at follow-up, and social validity ratings suggested that the
intervention was highly acceptable for both the students and their teachers. This
study contributes to the knowledge base on effective and feasible interventions to
support the inclusion of children with ASD in general education settings.

Keywords Compliance · Self-management · Autism · Multiple-baseline
experimental design · On-task behavior

* Angelika Anderson
angelika@waikato.ac.nz

1 Krongold Center, Faculty of Education, Monash University, Melbourne, VIC, Australia
2 School of Psychology, Faculty of Arts and Social Sciences, The University of Waikato, Private

Bag 3105, Hamilton 3240, New Zealand

http://orcid.org/0000-0003-1156-4066

http://crossmark.crossref.org/dialog/?doi=10.1007/s10864-019-09346-5&domain=pdf

Journal of Behavioral Education

1 3

Introduction

Compliance can be thought of as a skill that develops throughout childhood and for
the purposes of this study is defined as “acting in accordance with a directive to
engage in or to stop engaging in a behaviour” (Owen et al. 2012, p. 364). Low levels
of compliance can be accompanied by behaviors of concern often referred to in the
literature as non-compliant behaviors (such as refusal or defiance; Kochanska and
Aksan 1995). This can be confusing as the prefix suggests an absence of a behavior
or skill, yet often the term non-compliance is used to indicate behavioral excesses. In
this report, we will refer to compliance as a skill and non-compliance as its absence.

Of significance, non-compliance is a problem commonly reported by those who
work with children with autism spectrum disorder (ASD; Soto-Chodiman et  al.
2012; Van Bourgondien 1993). Interestingly, some of the defining characteristics
of ASD, such as deficits in social and communication skills, may make it difficult
for such children to comply with common instructions. Within school environ-
ments, persistent low levels of compliance can adversely impact a student’s ability
to develop appropriate social and academic skills (Austin and Agar 2005). Further,
compliance problems can cause significant distress for teachers (Aloe et  al. 2014)
and limit educational opportunities for other students in the class (de Martini-Scully
et al. 2000).

A number of intervention programs have been developed to address non-compli-
ance in young children. Three commonly utilized strategies to enhance compliance
are the high-probability command sequence (HPCS; Nevin 1996), errorless compli-
ance training (ECT; Ducharme 1996), and effective instruction delivery (EID; Ford
et al. 2001). Despite improved rates of compliance being reported in some instances,
research on each has yielded mixed results and identified clear limitations to their
application in applied settings, such as schools (Banda et  al. 2003; Ducharme and
Shecter 2011; Lui et al. 2014). Common limitations include a requirement for skill-
ful procedural implementation by others (e.g., parents or teachers) and low rates of
treatment fidelity.

Given the limitations of these strategies in academic settings, further research
exploring treatment alternatives appears warranted. Ideally, such strategies should
be characterized by ease of implementation, with reduced reliance on prompting
by adults. Preferably, these characteristics would make interventions attractive for
teachers allowing them more time for academic instruction and requiring less for
behavior management.

One strategy that may satisfy these criteria is self-management. Self-manage-
ment has been defined as “… the personal application of behavior-change tactics
that produces a desired change in behavior” (Cooper et al. 2007, p. 586). A well-
established, evidence-based procedure (National Autism Center 2009), including
in primary schools (Briesch et  al. 2019; Busacca et  al. 2015; Carr et  al. 2014;
Moore et al. 2013), self-management commonly includes strategies such as self-
monitoring, self-evaluation, and self-reinforcement. Self-monitoring is a strategy
whereby a person systematically observes his/her behavior and records the occur-
rence or non-occurrence of the target behavior, self-evaluation involves creating

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Journal of Behavioral Education

a behavioral goal and evaluating one’s own performance against a predetermined
goal, and self-reinforcement includes the delivery of a reward contingent upon
meeting a predetermined goal. These strategies can be used in isolation, but often
they are used in combination with at least one other strategy (Briesch et al. 2019;
Cooper et al. 2007). Self-management is considered to be a pivotal skill that may
generate widespread behavioral gains (Koegel et  al. 1999). Self-management
interventions have been reported to be effective in schools (Busacca et al. 2015),
in improving both on-task behavior and academic performance of children with
ADHD (Slattery et  al. 2016) and children with ASD (Carr et  al. 2014). Indeed,
a meta-analysis conducted by Lee et  al. (2007) reported self-management effec-
tive in increasing the frequency of appropriate behavior of students with ASD,
in a variety of settings and conditions. Further, Wilkinson (2008) reported that
students who acquired self-management skills displayed enhanced academic and
social skills as a by-product of being able to manage their own behavior more
effectively. Scruggs and Mastropieri (1998), in a review article describing the
implementation of self-management interventions for children with ASD in gen-
eral education schools, cites evidence for increased independence and compe-
tence as additional benefits of self-management.

There is, however, limited research investigating the utility of self-management
interventions to increase compliance rates in individuals with ASD, although the
studies that have been conducted demonstrated potential for further investigation.
For example, Wilkinson (2008) used a self-management procedure to increase com-
pliance rates in a 9-year-old student with Asperger’s syndrome in a general edu-
cation school setting while Lui et  al. (2014) investigated the effectiveness of self-
management strategies in improving compliance with parental requests in the home
setting. In the Lui et al. study, the young boys diagnosed with either Asperger’s syn-
drome or ASD all showed marked improvements in compliance and a reduction in
problem behavior on implementation of the procedure with their parents reporting
the intervention to be both acceptable and easy to implement. This study also pro-
vided support for the notion that self-management was a pivotal skill, since all the
children displayed improvements in untargeted problem behaviors and their parents
reported increased independent functioning among their children.

Although self-management is considered an effective intervention, gaps in the lit-
erature have been identified. Few studies have been conducted with younger children
with ASD in general education settings (Aljadeff-Abergel et al. 2015). For example,
Busacca et al. (2015) in their review of 16 studies found a paucity of self-manage-
ment research conducted on students in Grades 1 and 2, with a particular lack of
quality studies investigating the effectiveness of self-management interventions for
students with ASD, learning disabilities, and emotional and/or behavioral disorders.
Further, a lack of research investigating the effectiveness of self-management in
increasing compliance among children with ASD has been noted (Aljadeff-Abergel
et al. 2015; Busacca et al. 2015). At the same time, the educational needs of these
students are increasingly having to be met in general education classrooms by teach-
ers who often feel ill prepared to meet their needs (de Boer et  al. 2011). There is
therefore a need for effective procedures that are easy to implement in schools and
that do not place excessive demands on teachers.

Journal of Behavioral Education
1 3

The purpose of this study, therefore, was to investigate the effectiveness of a self-
management intervention for two students diagnosed with ASD and ADHD in a
Grade 2 regular class setting. It was anticipated that the implementation of self-man-
agement would result in improved compliance, along with an expectation that there
would be a concomitant improvement in on-task behavior. It was also predicted that
compliance and on-task behavior would be maintained following fading of the inter-
vention and that both the students and the teachers would consider the self-manage-
ment intervention socially valid.

Methods

Ethics approval was obtained from the University Research Ethics Committee before
recruitment took place. The school principals also gave permission to conduct the
study in their schools, and the participants, their parents, and teachers gave informed
consent for participating in the project prior to data collection.

Recruitment and Selection

Information about the research project was advertised on a state-level ASD peak
body research registry. Interested parents contacted the researchers before undergo-
ing a brief phone interview to screen for their children’s suitability for the project.
Eligibility criteria for participation were: The child has a diagnosis of autism spec-
trum disorder, is enrolled in the general education system, either in a private or cath-
olic school, and in Grade 1 or 2. The teacher of the child has previously told parents
that they have difficulty getting the child to follow instructions at school.

After consent and permission were obtained, more detailed participant informa-
tion was gained in a face-to-face interview with the teachers.

Participants

The participants in this study were two 8-year-old boys. The first participant was a
Grade 2 student in a private school in Melbourne. According to his parents, Sean
(pseudonym) was diagnosed with Asperger’s disorder at 4.5  years old and, subse-
quently, also with attention-deficit/hyperactivity disorder (ADHD). Since the diag-
nosis, Sean had been receiving therapy based on the principles of applied behav-
ior analysis (ABA). Sean was also on medication (risperidone and fluoxetine) at
the point when his parents approached the researchers. Despite the interventions
received, Sean’s mother reported that he had difficulty following instructions at
school. At school, Sean received additional individual in-class support from his
ABA therapist (once a week, 40 min), parent volunteers (once a week, 60 min), and
an integration aide (three times per week, 80 min each session on average) through-
out the project duration. These additional support personnel were mostly present
during English classes, when Sean tended to display non-compliant and more off-
task behavior.

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Journal of Behavioral Education

The second participant was a Grade 2 student at a different private school in Mel-
bourne. John (pseudonym) was diagnosed with ASD at 6 years old and subsequently
also with ADHD. According to the psychological, medical, and speech pathology
reports provided by his parents, he had a complex medical history including prema-
turity, global development delay, oro-motor (non-speech) and sensory related dif-
ficulties, along with pragmatic language disorder. Since diagnosis, John had been
receiving extensive behavior therapy, social skills training, and speech therapy
from various agencies. He was also on medication at the point when his parents
approached the researchers. Despite the interventions and medications received,
John’s mother reported that he had difficulty following instructions at school. At
school, John received in-class support from an integration aide before and during the
project. The integration aide was mostly present during reading and writing classes
where John was reported to have difficulty attending and staying on task.

Settings

Most observations and the intervention were conducted in the participants’ class-
rooms. Sean’s class consisted of 17 students. Classes generally were of 40-min dura-
tion, though occasionally activities would stretch beyond 40  min if multiple con-
secutive slots were allocated for a subject. A class typically began with whole class
instruction by the teacher, with students seated on the floor at the front of the room.
This was followed by independent or group work at the students’ desks. The use of
technology such as smartboards, iPads, and computers was common in the class-
room. Due to a term change, there were slight changes in the class schedule and
support structure over the course of the study. Three subject areas were identified
for intervention: Math, English, and English support classes. English support classes
were phonics lessons in a smaller class of eight students in a different classroom
with a different teacher.

John was in a class of 24 students. Classes were of 50-min duration, and occa-
sionally joint activities were held across two classes that stretched beyond 50 min
if consecutive slots were allocated for a subject. A class typically began with whole
class instruction on the floor where the teacher explained the task by giving an
example and asking questions. This was followed by students working on the task
at their desks. Following preliminary observations and consultations with the class
teacher, three subjects were targeted for intervention: reading, writing, and numer-
acy classes.

Materials

Criteria Checklist

A checklist consisting of five questions was used as a semi-structured interview
protocol when interested parents initially contacted the researcher for eligibility
screening.

Journal of Behavioral Education
1 3

Event Recording form for Compliance

Event recording forms were created to record observed instances of compliance and
non-compliance with instructions in each session.

Momentary Time Sample Recording form for On‑Task Behavior

Momentary time sampling forms were created to record on-task behavior in each
session. The form also allowed the researcher to collect the data on on-task behav-
ior of two other students in the classroom for a normative comparison of on-task
behavior.

Story: Following Instructions

A 52-word story was created, based on Lui et  al. (2014) to describe what compli-
ance is. The story explains what instructions are (instructions are about things that
the teachers want me to do) and why it is good to follow instructions (e.g., When I
follow instructions, it makes me learn better.)

Role‑Play Situations

Between 15 and 20 role-play scripts were used during the teaching phase. They
were crafted based on the information gathered during preliminary observations and
included teacher requests such as “Take out your book” and “Let’s tidy up the table.”

Self‑Monitoring Sheet

Recording sheets were developed for the participants to self-record incidents of
compliance. For John, this was a reusable laminate in accordance with the school
policy on ecological sustainability. The recording sheets consisted of 20 blank boxes
(with hook and loop fasteners on the laminated version) where participants were
instructed to paste a smiley face for each instance of compliance. The sheets also
included spaces for the agreed goal and reward.

Treatment Fidelity Checklist

The checklist was created to measure the extent to which the five components of
the intervention package were delivered as planned during each intervention session.
(All the above materials are available from the first author on request.)

Behavior Intervention Rating Scale (BIRS)

The BIRS was created by Elliott and Treuting (1991) to assess teachers’ acceptabil-
ity and perceived effectiveness ratings of educational interventions. It consists of 24
items with a 6-point Likert scale. This form was used for Sean, while for John the
adapted version (BIRS-A) was used. The adapted version includes minor wording

1 3
Journal of Behavioral Education

changes so as to be able to use the scale as a pre- and post-measure (i.e., Item 1
“This would be…” = “This will be…..” in the adapted (pre-) version).

Children’s Intervention Rating Profile‑Adapted Version (CIRP‑A)

An adapted version of CIRP (Turco and Elliott 1986) was developed commensurate
with the participants’ age. Although CIRP-A is similar to the original version that
consists of seven items and a 6-point Likert scale, the adapted version included sim-
plified language and replacement of the Likert scale with emoji.

Independent and Dependent Variables

Independent Variable

The independent variable was a self-management intervention package consisting
of the following components: goal setting, discrimination teaching, self-monitoring,
self-recording, and reinforcement.

Dependent Variable

The level of compliance to instructions was the primary dependent variable. Com-
pliance was operationally defined as the initiation of a requested response within
10 secs of the teacher’s request. It was expressed as a percentage of the number of
instructions delivered within the observed period. It was calculated by dividing the
number of instructions complied with by the total number of instructions delivered
and multiplying by 100.

The concomitant measure was on-task behavior, operationally defined as behav-
iors that the student was expected to be engaged in at a particular moment in time.
These included behaviors such as sitting in seat, paying attention to the teacher when
the teacher is instructing (e.g., listening, eyes and face forward), and working on the
assignment. On-task behavior was calculated by dividing the number of observed
intervals on-task by the total number of observed intervals and multiplied by 100.

Experimental Design

A multiple-baseline across three settings (reading, writing, and numeracy classes for
John; and Math, English, and English support classes for Sean) design was used to
evaluate the functional relationship between the self-management intervention and
observed changes in the participants’ behavior. In this design, the baseline phase
was initiated simultaneously in all the three settings. Initiation of the intervention
phase was time-lagged sequentially across the subject areas.

Journal of Behavioral Education
1 3

Data Collection

Data were collected over the course of 3 months, three to four times a week for each
class.

Observation Procedure

Event recording was used to document incidents of the participants’ compliance
and non-compliance for the entire duration of each observation, whereas data on
on-task behavior were gathered using momentary time sampling of 10-s intervals
for a discrete activity typically the first activity with clear starting and ending points
that occurred during the observation. To make a normative comparison for on-task
behavior, data from two peers who happened to be near the target student were also
collected in each setting at the same time. Rates of on-task behavior were calcu-
lated by dividing the number of intervals observed with on-task behavior by the total
number of observation intervals, multiplied by 100%. The peers’ on-task behavior
data were calculated in the same manner and then averaged to provide a single esti-
mate for comparison.

Inter‑observer Agreement

Inter-observer agreement was assessed for 22–33% of the observation sessions
across all classes in both baseline and intervention phases, to measure the reliability
of the observations. The observations were taken independently but simultaneously.
Percent agreement was calculated using the interval by interval method and dividing
the number of agreements by the number of agreements plus disagreements, and
then multiplied the value by 100%. Agreements were defined as instances in which
both observers’ recordings matched interval by interval.

For John, the mean inter-observer agreement for compliance was 99% (range
80–100%), and on-task behavior mean was 96% (range 77–100%), and for Sean the
mean inter-observer agreement for compliance was 97% (91–100%), with the on-
task behavior mean at 86% (64–98%). According to Hartmann, Barrios, and Wood
(2004), minimum acceptable values of an overall inter-observer agreement should
range between 80 and 90%. Thus, overall satisfactory levels of inter-observer agree-
ments were obtained.

Treatment Fidelity

Treatment fidelity data were collected in each observation session during the inter-
vention and fading phases with the treatment fidelity checklist. Treatment fidelity
was measured to determine the extent to which the implementation of the interven-
tion adhered to planned procedures (Cooper et al. 2007). Adherence to the planned
procedures was calculated by dividing the number of steps completed correctly by
the total number of steps, with a calculated mean of 100% fidelity, meaning all steps
of the self-management procedure were followed correctly during all intervention
and fading sessions.

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Journal of Behavioral Education

Procedures

Preliminary Observations

Preliminary observations were conducted 2  days before the collection of baseline
data to select appropriate subject areas for intervention, to have a better understand-
ing of the participants’ behavior, to customize the teaching materials, and to allow
the students and teachers to become accustomed to the presence of the researchers in
their classrooms.

Baseline Phase

During baseline, unobtrusive observations were conducted to collect participants’
behavior data during typical daily routines in the respective classrooms and ses-
sions. Researchers positioned themselves at the rear of the classrooms, ignoring
any attempts by the students to interact or communicate with them. If a student
attempted to initiate interactions, the researcher pretended to be preoccupied, to
minimize the effect of the researcher’s presence in the classroom.

Following the completion of baseline, the researchers conducted a preference
assessment for John by way of interviews with him and his teacher to determine a
list of preferred items and activities that were also acceptable and feasible. Sean vol-
unteered his preferences informally.

Intervention Phase

The intervention phase consisted of two parts: training and self-monitoring.

Part 1: Training The aim of this was to teach participants to discriminate between
compliance and non-compliance and to train them to implement the self-management
procedures. Participants attended two 40-min training sessions across 2  days. The
training consisted of a series of steps, including: outlining the purpose of the study,
describing compliance as it related to the study, and using role-play to teach the dif-
ference between compliance and non-compliance. Then, we coached participants in
the use of the self-management procedure, by having them describe the procedure,
training them to use the self-monitoring tool, and providing opportunities to practice
the procedure. Training was completed once participants achieved at least 80% accu-
racy in self-recording in two consecutive model activities.

Part 2: Self‑Monitoring Five minutes prior to the beginning of the target obser-
vation class, the self-management goal and reward were set for the session in
consultation with participants and the teacher if appropriate. Participants were
then handed the self-monitoring sheet, on a clipboard. John also received stickers,
while Sean also received a pencil to self-record. During the class, a researcher
sat behind the participants to prompt them to self-record through either verbal or

Journal of Behavioral Education
1 3

nonverbal means (e.g., shoulder taps) if they did not self-record within 5 secs after
performing a compliant behavior. Non-compliance was ignored. At the end of the
class, the researcher collected the self-monitoring sheets from the participants and
reviewed them. If participants achieved the self-management goal for the class,
rewards were administered at the next convenient time.

Fading Phase

The fading procedure was based on that described in Lui et  al. (2014). Fading
involved the gradual withdrawal of the self-management procedure in each set-
ting once a steady high rate of compliance was achieved. For John, this was
accomplished by increasing the requirements to receive a reward. Next, the fre-
quency of using the self-monitoring tool was decreased until finally it was no
longer used. Instead of providing John with self-monitoring sheets, the researcher
instructed John to mentally tally the number of compliant acts during the class.
For Sean, the first step was omitted and fading began with the intermittent use of
the self-monitoring sheet as described above. All other steps (i.e., setting of goals
and rewards, review of performance, and administration of rewards) remained
unchanged. In addition, for both John and Sean, there was a shift of rewards from
tangible (e.g., free computer time after the class) to social reinforcements (e.g.,
verbal praise by the teacher).

Follow‑up

Follow-up observation sessions were conducted after the last observation of the
fading condition: 7–10 days later for John and 1 week later for Sean. The follow-
up sessions were conducted under baseline conditions.

Social Validity

Social validity in terms of the social significance of the goals, outcomes, and pro-
cedures of self-management (Wolf 1978) was assessed by asking the teachers and
participants to respond to the BIRS (BIRS-A for John) and the CIRP-A in base-
line and following the fading phase. Anecdotal social validity information was
also collected from the participants and their teachers throughout the study.

Data Analysis

The data were graphed and analyzed using visual analysis, the most common data
analytical technique in single-case experimental design (Busk and Marascuilo
2015). The effectiveness of the intervention was computed using the percentage
of non-overlapping data (PND) metric, this being the percent of intervention data
points that surpassed the highest baseline data point (Scruggs et al. 1987; see also
Scruggs and Mastropieri 1998). Although as yet there is no consensus regarding

1 3
Journal of Behavioral Education

the most appropriate procedure for estimating the effectiveness of interventions in
single-subject design studies (Parker et al. 2007), a recent review concluded that
the PND metric results in coherent and valid estimates of effectiveness of inter-
vention in single-subject research (Carr et  al. 2015). According to the standard
established by Scruggs et al. (1987), PND scores over 90% are interpreted as very
effective, 70% to 90% as effective, 50% to 70% as questionable, and below 50% as
ineffective treatments.

Results

The effectiveness of the self-management intervention was evaluated using visual
analysis. For within-condition analysis for both compliance and on-task behavior,
three elements were examined: level, trend, and variability of data. As per the guide-
lines by Cooper et al. (2007), the median was used instead of the mean in analyzing
compliance due to the presence of several extreme values in the data set. Analysis
of level, trend, and variability indicated that participants demonstrated increases in
compliance and on-task behavior from baseline to self-management intervention,
and the increases were maintained when the intervention was faded.

The percentage of Sean and John’s compliance is presented graphically in Figs. 1
and 2, respectively. Visual analysis indicates that the percentage of compliance
increased in both participants in association with the staggered implementation of
the intervention across all settings, and the increases maintained when the interven-
tion was faded.

Participant 1: Sean

Math

In the baseline phase, Sean showed a moderate, stable level of compliance with an
ascending trend (Mdn = 51.3%, range 44.4–55.6%). Upon the introduction of the
self-monitoring intervention, his compliance increased to a moderate-to-high level
with a median of 76.5% (69.2–84.6%), with a stable and slightly descending trend.
During the fading phase, his compliance remained at moderate-to-high levels with a
median compliance rate of 76.0% (72.2–80.9%) with a stable, descending trend. His
high compliance was maintained 1 week later at 90.0%.

English

Sean’s baseline compliance was at moderate-to-low levels with a steadily declining
trend (Mdn = 38.7%, range 22.7–42.9%). During the intervention phase, his compli-
ance improved to a median of 64.0% (57.1–70.8%), with an initially moderate-to-
high, ascending level of compliance followed by a high, stable level of compliance.
Due to the term change midway through the project, there was a change in the sup-
port schedule for Sean, such that he had adult support in every English class in the

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Intervention Fading Follow-upBaseline

English
Support

Staff support
increased

Fig. 1 Sean’s percentage of compliance across settings

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Fig. 2 John’s percentage of compliance across settings

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1 3

later part of the term. When both adult support and self-monitoring were in place,
Sean’s compliance increased further (Mdn = 85.8%, range 83.3–95.2%), with a high,
relatively stable descending trend. Although his compliance remained at a high
level, when the self-monitoring tool was withdrawn, Sean’s compliance showed an
unstable, descending trend with a median percentage of 83.0% (80.8% to 100%). His
rate of compliance at the 1-week follow-up was maintained at 91.7%.

English Support

Sean displayed a moderate-to-high, variable level of compliance during the base-
line observations (Mdn = 69.2%, range 45.0–77.8%). Upon the introduction of the
intervention, Sean’s compliance increased to a high level with a median of 92.3%
(90.0% to 94.6%), with a stable trend. However, due to school events and changes in
the class schedule, English support classes became very tentative; consequently, the
intervention was suspended due to the unpredictable schedule of the classes. Never-
theless, Sean’s compliance remained high at 92.9% at follow-up.

Participant 2: John

Reading

In baseline, John displayed a moderate level of compliance with a relatively stable
trend with a median of 78.5% (range 66.7–80.0%). Upon the implementation of
self-management, his level of compliance improved gradually to a median of 86.6%
(range 71.4–100%), with a slight variable trend evident. During fading, John’s
level of compliance was maintained at a high level with a median of 100% (range
85.7–100%) with maximum possible scores over the first two and the last fading
sessions. At 10-day follow-up, John maintained a high level of compliance at 88.9%.

Writing

John displayed variable moderate baseline levels of compliance during the base-
line phase with a median of 65.2% (range 50–82.4%). No clear trend was visible.
When self-management was implemented, there was an abrupt increase in his level
of compliance with no overlap with baseline data. John scored a high level of com-
pliance (Mdn = 94.0%, range 88.9–100%), with a stable trend. During fading, John
maintained a high level of compliance with maximum possible scores on the first
and the last fading sessions with a median percentage of 100% (range 88.9–100%).
Similar to the reading class, John maintained high levels of compliance at 93.8% at
follow-up.

Numeracy

During the baseline phase, John displayed a relatively moderate level of compliance
with a median of 56.4% (range 33.3–86.0%) in the numeracy class. There was no

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Journal of Behavioral Education

detectable trend due to high variability. Implementation of self-management led to
a marked increase in the level of compliance (Mdn = 94.1%; range 89.0–100%) with
a slightly descending trend but with no overlap with baseline data. Similar to the
reading and writing classes, when the self-monitoring tool was gradually withdrawn
and eventually removed, John’s level of compliance maintained with a median of
91.7% (range 90.9–100%). His level of compliance at 10-day follow-up maintained
at 91.7%.

Effectiveness of Intervention

For Sean, the PND scores of the intervention in all settings (i.e., Math, English, and
English support) were 100%. According to Scruggs et al. (1987) standard, interven-
tions with PND scores over 90% are considered very effective. Thus, it can be con-
cluded that for Sean the introduction of the self-management intervention was very
effective in increasing compliance in all settings.

For John, the PND score of the intervention during reading class was 80.0%,
which indicates that the intervention was effective by the Scruggs et al. (1987)stand-
ards, in increasing John’s level of compliance. The implementation of self-man-
agement was very effective in increasing compliance during writing and numeracy
classes (PND scores for both = 100%). Calculated across the three intervention set-
tings, the average PND effect size was 93.3%, suggesting that the introduction of
self-management was very effective in increasing John’s compliance.

Figures 3 and 4 show the percentages of on-task behavior for Sean and John, and
their peers, respectively.

Participant 1: Sean

Math The level of Sean’s and his peers’ on-task behavior showed obvious varia-
tion across the days. Sean consistently displayed less on-task behavior (Mdn = 48.0%,
range 10.0–65.0%) than his peers (Mdn = 88.0%, range 75.0–97.5%) during the
baseline observations, with a median percentage difference of − 40.0% (range
− 65.0 to − 30.0%). When self-management was introduced, the gap between Sean
(Mdn = 66.7%, range 27.3–100%) and his peers (Mdn = 77.3%, range 55.9–87.2%)
reduced, with a median percentage difference of − 3.6% (range − 40.9% to 22.7%).
There were two occasions (Days 12 and 16) when Sean was more on-task than his
peers. A possible explanation for the outlier data points on Day 19, for both Sean
and his peers is that there was a school event immediately after the class. During
the fading phase, Sean was consistently more on-task (Mdn = 60%, range 50–66.7%)
compared to his peers (Mdn = 45.8%, range 36.7–56.7%), with a median percentage
difference of 10% (range 4.2–23.3%).

English Similar to the data from Math, there was a consistent difference in lev-
els of on-task behavior between Sean (Mdn = 47.9%, range 23.1–56.3%) and his
peers (Mdn = 75%, range 53.9–90%) during the baseline phase, with a median per-
centage difference of − 30% (range − 40 to − 18.8%). When self-management was

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Fig. 3 Percentages of Sean’s and his peers’ on-task behavior across settings

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Fig. 4 Percentages of John’s and his peers’ on-task behavior across settings

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1 3

introduced, Sean’s on-task behavior (Mdn = 68.3%, range 60–100%) became com-
parable to that of his peers (Mdn = 71.7%, range 46.4–85.7%), with a median per-
centage difference of 2.5% (range − 16.7 to 38.7%). With adult support, his on-task
behavior continued to be comparable, or better than his peers. When the use of the
self-monitoring tool was faded out, Sean’s on-task behavior (Mdn = 85.8%, range
66.7–94.4%) remained comparable to or higher than that of his peers (Mdn = 63.9%,
range 50–80%), with a median percentage difference of 20.8% (range 0–33.3%).

English Support Sean’s (Mdn = 57.1%, range 40–85%) and his peers’ (Mdn = 76.7%,
range 60.7–95.5%) on-task behavior was very variable during the baseline phase
with a median percentage difference of − 10.7% (range − 50 to 17.5%). Sean was
less on-task than his peers before Day 5, the gap narrowed from Day 8 onwards,
making his on-task behavior comparable to his peers. This coincided with the
introduction of the self-management intervention during the Math class on the
same day. It appeared that there could be some generalization effect across settings
with the use of the self-monitoring tool. When self-management was introduced,
Sean (Mdn = 68.5%, range 64.3–72.7%) was consistently more on-task than his
peers (Mdn = 42.4%, range 39.3–45.5%). The median percentage difference in the
intervention phase was 26.1% (range 25–27.3%). However, the intervention was
suspended due to the unpredictable schedule of the classes.

Participant 2: John

Reading During the Baseline phase, John consistently displayed lower levels of
on-task behavior (Mdn = 50%, range 43–88.9%) than his peers (Mdn = 71.3%, range
54.5–100%) with a median percentage difference of − 12% (range − 26.3% to − 9.5%)
and with an increasing trend evident for both John and his peers With the introduction
of self-management, the gap narrowed, making John’s on-task behavior (Mdn = 86%,
range 52.5–100%) comparable to his peers (Mdn = 82.6%, range 73.8–93.4%) with
a median percentage difference of 2.1% (range − 25.5–7.5%). When the use of the
self-monitoring tool was faded out, John’s on-task behavior remained comparable
(Mdn = 85.8%, range 78.9–100%) to his peers (Mdn = 89.1%, range 68.8–98.5%),
with a median percentage difference of − 4.2% (range − 11.1% to 10.1%).

Writing Similar to the data from reading, both John and his peers showed con-
siderable variability in on-task behavior during the baseline phase. While John
displayed a relatively stable moderate level of on-task behavior (Mdn = 55%, range
45–65%), his peers displayed a moderate-to-high level of on-task behavior with
some variability (Mdn = 75.7%, range 47–81.5%). There was a median percentage
difference of − 15.8% (range − 30.5% to 8%). Although overall John displayed less
on-task behavior than his peers, there was one occasion (session 18) where he was
more on-task than his peers. With the introduction of self-management, John’s
on-task behavior improved and stabilized (Mdn = 90%, range 87–92.5%), and he
was consistently more on-task than his peers (Mdn = 76.3%, range 68.9–80.3%),

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with a median percentage difference of 16% (range 6.7–21.1%). When the use of
the self-monitoring tool was faded out, John’s on-task behavior (Mdn = 85%, range
77.5–88.9%) remained comparable to, or higher than his peers’ on-task behavior
(Mdn = 77.8%, range 65.2–84.2%), with a median percentage difference of 11.1%
(range 0.8–12.3%).

Numeracy Similar to the data from both reading and writing, the level of John’s and
his peers’ on-task behavior showed considerable variation during the baseline obser-
vations. John was consistently less on-task (Mdn = 61.6%, range 45–80%) than his
peers (Mdn = 82.5%, range 59.5–98%) with a median percentage difference of − 18%
(range − 37% to 4.5%). When self-management was introduced, John displayed a
high level of on-task behavior with reduced in-phase variability (Mdn = 90.9%, range
83–97.2%). While his peers displayed a stable and high level of on-task behavior
(Mdn = 78.5%, range 77.8–82.5%), John stayed consistently more on-task than his
peers. The median percentage difference in the intervention phase was 10% (range
5.2–19.4%). During the fading phase, John maintained higher on-task behavior
(Mdn = 90%, range 85–92.5%) than his peers (Mdn = 77.6%, range 69–88.8%), with a
median percentage difference of 12.4% (range 3.8–16%).

The prediction that compliance and on-task behavior would be maintained fol-
lowing fading of the intervention was met. For Sean, in math his high compliance
was maintained 1 week later at 90.0%, while in English his rate of compliance at the
1-week follow-up was maintained at 91.7% and in English support after a 1-week
follow-up, he remained more on-task than his peers, with a percentage difference of
23.8%.

For John, in the reading task at 10-day follow-up, he remained more on-task than
his peers, with a percentage difference of 10%. The intervention effect in the writing
activity was maintained at 10-day follow-up with a percentage difference of 13.2%,
while in numeracy, similar to the other two settings, John continued to engage in
more on-task behavior than his peers at 10-day follow-up with a percentage differ-
ence of 15%.

Social Validity
Participant 1: Sean

Sean expressed an overall favorable view of the study before and after the interven-
tion. He continued to like the intervention (Item 6) and believed that the intervention
would help him do better in school (Item 7). Sean found the intervention to be dif-
ficult, as indicated by a 3-point change in his ratings on pre- and post-measures on
Item 3 “The method is too hard on me.”

Despite the already-positive ratings on the pre-measure, his teacher’s ratings on
the post-measure increased by 1–2 points for 17 out of the 24 items on the rating
scale. There was a change in polarity of the teacher’s perception of the acceptability
of intervention (Items 3 and 4), lasting impact of intervention (Items 17 and 20),

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magnitude (Item 18), and rate of behavioral improvement (Items 16 and 19), with
more favorable ratings on the post-measure.

Participant 2: John

Overall, there was evidence that the intervention procedure was socially acceptable
for both John and his teacher. John held a favorable view of the study before and
after the intervention. He continued to like the intervention (Item 6) and believed
that the intervention would help him do better in school (Item 7). At the start of the
study, John was concerned that the self-management procedure would cause some
problems with his peers as indicated by “I agree” rating on Item 3. His post-measure
rating on the same item shifted to “I do not agree,” and he mentioned that the proce-
dure hardly caused any issue with his peers.

His teacher’s perceptions of the intervention were positive at the beginning of
the study. Upon completion of the last fading session, her ratings on the post-meas-
ure increased by 1 point for eight items and decreased for one item. Her rating for
Item 6 “Most teachers would find this intervention suitable for the behavior problem
described” shifted from “Strongly Agree” to “Agree.” Regarding this change, she
commented that she was concerned about the duration of the intervention as she
believed it was a bit time-consuming for the general education classroom. Neverthe-
less, her rating on the post-measure indicated that she was satisfied with the out-
comes, found the intervention acceptable, and considered the goals socially valid.

Discussion

The purpose of this study was to investigate the effectiveness of self-management
for two Grade 2 students diagnosed with ASD and ADHD in general education Aus-
tralian primary schools. The results support the hypotheses that the implementa-
tion of self-management would result in improved compliance, be associated with
a concomitant improvement in on-task behavior, that these changes would maintain
following the fading of the intervention, and both participants and their teachers
would consider self-management socially valid. These findings are consistent with
the results of a previous study on increasing compliance and on-task behavior in a
student with Asperger’s syndrome using a self-management intervention (Wilkinson
2008).

The degree of improvement in compliance was socially significant, in line with
normative expectations of between 60 and 90%, as suggested by McMahon and
Forehand (2003). Furthermore, effect size calculations suggest that self-manage-
ment met the required standard to be classified as very effective for both participants
in increasing the primary target behavior, compliance, in all settings. This finding
should be treated with caution because with one participant, Sean, data available for
one setting, English support, fails to meet the minimum criterion of three data points
per phase set by the What Works Clearinghouse (Kratochwill et al. 2010).

In addition to the increase in compliance, the intervention was associated with
concomitant improvements in on-task behavior, such that the students’ on-task

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behavior was comparable to, or better than, that of their peers. The high levels of
compliance and on-task behavior were maintained at follow-up across all settings for
both participants.

In addition to being highly effective, the intervention was also deemed acceptable
by both teachers and students. Even though both teachers and students were positive
about the intervention before the study commenced, their ratings of the intervention
further increased by the end of the study.

Limitations

Several considerations are relevant in the extrapolation of these findings to other
situations. First, the usual limitations regarding the external validity of the findings
apply. Systematic replication is, therefore, recommended.

Secondly, a potential confound occurred in Sean’s case during the intervention
phase in English where following two intervention data points and a short vacation
break, an additional teacher was introduced to the classroom. Though there was an
immediate experimental effect on introduction of the intervention for Sean in this
condition, the further elevated compliance rates after the break cannot be attributed
to the self-management intervention alone.

A further limitation for Sean in the English support class was that only two inter-
vention data points were able to be collected. Unfortunately, such limitations are not
uncommon in applied work of this nature (see, for example, Roberts et al. 2019).

Theoretical Implication

The approach to addressing non-compliance in this study was based on a skill-deficit
paradigm, where rather than aiming to reduce non-compliance we sought to increase
rates of compliance. This is in line with Skinner’s analysis of verbal behavior where
compliance can be seen as a listener skill (Cooper et al. 2007). The results support
the notion that compliance is a skill that can be taught. This has theoretical and prac-
tical implications. Researchers and therapists might focus more on increasing com-
pliance rather than seeking to reduce non-compliant behaviors.

Future Direction

While the results from this study suggest that self-management is an effective and
socially valid way of increasing compliance in junior elementary students with
ASD, the social validity of the procedure could be enhanced. One way of doing this
could be by incorporating wearable technology to facilitate self-recording. Sean
expressed his concern numerous times of not wanting to appear different from his
peers and was initially hesitant about using the paper and pencil-based self-mon-
itoring tool during the intervention phase. Paper and pencil-based self-monitor-
ing also has limitations in classroom settings that are more dynamic and interac-
tive (where children move around the classroom, or engage in activities that are
not table-based). Wearable technology (such as smartwatches) in conjunction with

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appropriate self-monitoring apps could overcome these limitations. Coupled with
some kind of prompting mechanism, they could also facilitate greater independence
from the researcher. The self-recording process would be more discrete. Busacca
et al. (2016) have recently piloted the use of such technology in the classroom, and
behavioral improvements and high social validity were observed. Future research
exploring the effects and the acceptability of such technology in self-management is
clearly warranted.

Conclusion

Compliance is regarded as a critical skill for academic and social success at school
(Ducharme and Shecter 2011). Not only did this study demonstrate the acceptability
and effectiveness of self-management in increasing compliance and on-task behav-
ior in young children with ASD in primary school settings, the potential benefit of
teaching self-management in order to empower the student in managing his or her
own behavior, while decreasing the teacher’s role in behavior management, was evi-
dent. In classrooms where teachers are often overwhelmed with instructional and
classroom management demands, this intervention could play a role in alleviating
teachers’ stress and, in turn, fostering the inclusion of students with special educa-
tional needs in regular general education classrooms.

References

Aljadeff-Abergel, E., Schenk, Y., Walmsley, C., Peterson, S., Frieder, J., & Acker, N. (2015). The effec-
tiveness of self-management interventions for children with autism—A literature review. Research
in Autism Spectrum Disorders, 18, 34–50. https ://doi.org/10.1016/j.rasd.2015.07.001.

Aloe, A. M., Shisler, S. M., Norris, B. D., Nickerson, A. B., & Rinker, T. W. (2014). A multivariate meta-
analysis of student misbehavior and teacher burnout. Educational Research Review, 12, 30–44. https
://doi.org/10.1016/j.edure v.2014.05.003.

Austin, J. L., & Agar, G. (2005). Helping young children follow their teachers’ directions: The utility of
high probability command sequences in pre-k and kindergarten classrooms. Education and Treat-
ment of Children, 28(3), 222–236.

Banda, D. R., Neisworth, J. T., & Lee, D. L. (2003). High-probability request sequences and young
children: Enhancing compliance. Child & Family Behavior Therapy, 25(2), 17–29. https ://doi.
org/10.1300/J019v 25n02 _02.

Briesch, A. M., Daniels, B., & Beneville, M. (2019). Unpacking the term “self-management”: Under-
standing intervention applications within the school-based literature. Journal of Behavioral Educa-
tion, 28(1), 54–77. https ://doi.org/10.1007/s1086 4-018-9303-1.

Busacca, M., Anderson, A., & Moore, D. W. (2015). Self-management for primary school students dem-
onstrating problem behavior in regular classrooms: Evidence review of single-case design research.
Journal of Behavioral Education, 24(4), 373–401. https ://doi.org/10.1007/s1086 4-015-9230-3.

Busacca, M. L., Anderson, A., & Moore, D. W. (September, 2016). Implementation of technology-based
self-management to improve on-task behaviour in primary school students: Self Management Assis-
tive technology (SMAT). In A pilot study presented at the 8th conference of the European Associa-
tion for Behaviour Analysis, Enna, Italy.

Busk, P. L., & Marascuilo, L. A. (2015). Statistical analysis in single-case research: Issues, procedures,
and recommendations, with applications to multiple behaviors. In T. R. Kratochwill & J. R. Levin
(Eds.), Single-case research design and analysis: New directions for psychology and education (pp.
159–186). New York, NY: Routledge.

https://doi.org/10.1016/j.rasd.2015.07.001

https://doi.org/10.1016/j.edurev.2014.05.003

https://doi.org/10.1016/j.edurev.2014.05.003

https://doi.org/10.1300/J019v25n02_02

https://doi.org/10.1300/J019v25n02_02

https://doi.org/10.1007/s10864-018-9303-1

https://doi.org/10.1007/s10864-015-9230-3

1 3
Journal of Behavioral Education

Carr, M. E., Anderson, A., Moore, D. W., & Evans, W. H. (2015). How should we determine treatment
effectiveness with single-case design research for participants with autism spectrum disorder?
Review Journal of Autism and Developmental Disorders, 2(1), 8–18. https ://doi.org/10.1007/s4048
9-014-0030-9.

Carr, M., Moore, D. W., & Anderson, A. (2014). Self-management interventions on students with
autism: A meta-analysis of single-subject research. Exceptional Children, 81(1), 28–44. https ://doi.
org/10.1177/00144 02914 53223 5.

Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). New Jersey:
Pearson Merrill Prentice Hall.

De Boer, A., Pijl, S. J., & Minnaert, A. (2011). Regular primary schoolteachers’ attitudes towards inclu-
sive education: A review of the literature. International Journal of Inclusive Education, 15(3), 331–
353. https ://doi.org/10.1080/13603 11090 30300 89.

de Martini-Scully, D. D., Bray, M. A., & Kehle, T. J. (2000). A packaged intervention to reduce disrup-
tive behaviors in general education students. Psychology in the Schools, 37(2), 149–156. https ://doi.
org/10.1002/(SICI)1520-6807.

Ducharme, J. M. (1996). Errorless compliance training: Optimizing clinical efficacy. Behavior Modifica-
tion, 20(3), 259–280. https ://doi.org/10.1177/01454 45596 02030 02.

Ducharme, J. M., & Shecter, C. (2011). Bridging the gap between clinical and classroom intervention:
Keystone approaches for students with challenging behavior. School Psychology Review, 40(2),
257–274.

Elliott, S. N., & Treuting, M. V. B. (1991). The behavior intervention rating scale: Development and
validation of a pretreatment acceptability and effectiveness measure. Journal of School Psychology,
29(1), 43–51. https ://doi.org/10.1016/0022-4405(91)90014 -I.

Ford, A., Olmi, D., Edwards, R., & Tingstrom, D. (2001). The sequential introduction of compliance
training components with elementary-aged children in general education classroom settings. School
Psychology Quarterly, 16(2), 142–157. https ://doi.org/10.1521/scpq.16.2.142.18702 .

Hartmann, D. P., Barrios, B. A., & Wood, D. D. (2004). Principles of behavioral observations. In M.
Hersen (Ed.), Comprehensive handbook of psychological assessment, behavioral assessment. New
Jersey, NJ: Wiley.

Kochanska, G., & Aksan, N. (1995). Mother-child mutually positive affect, the quality of child compli-
ance to requests and prohibitions, and maternal control as correlates of early internalization. Child
Development, 66(1), 236–254. https ://doi.org/10.2307/11312 03.

Koegel, R. L., Koegel, L. K., & Carter, C. M. (1999). Pivotal teaching interactions for children with
autism. School Psychology Review, 28(4), 576–594.

Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., et  al.
(2010). Single-case designs technical documentation. Retrieved August 30, 2019, from https ://ies.
ed.gov/ncee/wwc/Docum ent/229.

Lee, S., Simpson, R. L., & Shogren, K. A. (2007). Effects and implications of self-management for stu-
dents with autism: A meta-analysis. Focus on Autism & Other Developmental Disabilities, 22(1),
2–13. https ://doi.org/10.1177/10883 57607 02200 10101 .

Lui, C., Moore, D., & Anderson, A. (2014). Using a self-management intervention to increase com-
pliance in children with ASD. Child & Family Behavior Therapy, 36(4), 259–279. https ://doi.
org/10.1080/07317 107.2014.96761 3.

McMahon, R. R., & Forehand, R. L. (2003). Helping the noncompliant child. New York: The Guilford
Press.

Moore, D. W., Anderson, A., Glassenbury, M., Lang, R., & Didden, R. (2013). Increasing on-task
behavior in students in a regular classroom: Effectiveness of a self-management procedure using
a tactile prompt. Journal of Behavioral Education, 22(4), 302–311. https ://doi.org/10.1007/s1086
4-013-9180-6.

National Autism Center. (2009). National standards project: Addressing the need for evidence-based
practice guidelines for autism spectrum disorders. Randolph, MA: National Autism Centre.

Nevin, J. A. (1996). The momentum of compliance. Journal of Applied Behavior Analysis, 29(4), 535–
547. https ://doi.org/10.1901/jaba.1996.29-535.

Owen, D. J., Slep, A. M., & Heyman, R. E. (2012). The effect of praise, positive nonverbal response, rep-
rimand, and negative nonverbal response on child compliance: A systematic review. Clinical Child
and Family Psychology Review, 15(4), 364–385. https ://doi.org/10.1016/S0005 -7894(78)80009 -4.

Parker, R., Hagan-Burke, S., & Vannest, K. (2007). Percentage of all non-overlapping data (PAND). The
Journal of Special Education, 40(4), 194–204. https ://doi.org/10.1177/00224 66907 04000 40101 .

https://doi.org/10.1007/s40489-014-0030-9

https://doi.org/10.1007/s40489-014-0030-9

https://doi.org/10.1177/0014402914532235

https://doi.org/10.1177/0014402914532235

https://doi.org/10.1080/13603110903030089

https://doi.org/10.1002/(SICI)1520-6807

https://doi.org/10.1002/(SICI)1520-6807

https://doi.org/10.1177/01454455960203002

https://doi.org/10.1016/0022-4405(91)90014-I

https://doi.org/10.1521/scpq.16.2.142.18702

https://doi.org/10.2307/1131203

https://ies.ed.gov/ncee/wwc/Document/229

https://ies.ed.gov/ncee/wwc/Document/229

https://doi.org/10.1177/10883576070220010101

https://doi.org/10.1080/07317107.2014.967613

https://doi.org/10.1080/07317107.2014.967613

https://doi.org/10.1007/s10864-013-9180-6

https://doi.org/10.1007/s10864-013-9180-6

https://doi.org/10.1901/jaba.1996.29-535

https://doi.org/10.1016/S0005-7894(78)80009-4

https://doi.org/10.1177/00224669070400040101

Journal of Behavioral Education
1 3

Roberts, G. J., Mize, M., Reutebuch, C. K., Falcomata, T., Capin, P., & Steelman, B. L. (2019). Effects of
a self-management with peer training intervention on academic engagement for high school students
with autism spectrum disorder. Journal of Behavioral Education. https ://doi.org/10.1007/s1086
4-018-09317 -2.

Scruggs, T. E., & Mastropieri, M. A. (1998). Summarizing single-subject research: Issues and applica-
tions. Behavior Modification, 22(3), 22l–242. https ://doi.org/10.1177/01454 45598 02230 01.

Scruggs, T. E., Mastropieri, M. A., & Casto, G. (1987). The quantitative synthesis of single-subject
research: Methodology and validation. Remedial and Special Education, 8(2), 24–33. https ://doi.
org/10.1177/07419 32587 00800 206.

Slattery, L., Crosland, K., & Iovannone, R. (2016). An evaluation of a self-management intervention to
increase on-task behavior with individuals diagnosed with attention-deficit/hyperactivity disorder.
Journal of Positive Behavior Interventions, 18(3), 168–179. https ://doi.org/10.1177/10983 00715
58828 2.

Soto-Chodiman, R., Pooley, J. A., Cohen, L., & Taylor, M. F. (2012). Students with ASD in mainstream
primary education settings: Teachers’ experiences in Western Australian classrooms. Australasian
Journal of Special Education, 36(1), 97–111. https ://doi.org/10.1017/jse.2012.10.

Turco, T., & Elliott, S. (1986). Assessment of students’ acceptability ratings of teacher-initiated inter-
ventions for classroom misbehavior. Journal of School Psychology, 24(3), 277–283. https ://doi.
org/10.1016/0022-4405(86)90060 -9.

Van Bourgondien, M. E. (1993). Behavior management in the preschool years. In E. Schopler, M. E. Van
Bourgondien, & M. M. Bristol (Eds.), Preschool issues in autism (pp. 129–145). New York, NY:
Plenum.

Wilkinson, L. A. (2008). Self-management for children with high-functioning autism spectrum disorders.
Intervention in School and Clinic, 43(3), 150–157. https ://doi.org/10.1177/10534 51207 31161 3.

Wolf, M. M. (1978). Social validity: The case for subjective measurement or how applied behavior
analysis is finding its heart. Journal of Applied Behavior Analysis, 11(2), 203–214. https ://doi.
org/10.1901/jaba.1978.11-203.

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.

https://doi.org/10.1007/s10864-018-09317-2

https://doi.org/10.1007/s10864-018-09317-2

https://doi.org/10.1177/01454455980223001

https://doi.org/10.1177/074193258700800206

https://doi.org/10.1177/074193258700800206

https://doi.org/10.1177/1098300715588282

https://doi.org/10.1177/1098300715588282

https://doi.org/10.1017/jse.2012.10

https://doi.org/10.1016/0022-4405(86)90060-9

https://doi.org/10.1016/0022-4405(86)90060-9

https://doi.org/10.1177/1053451207311613

https://doi.org/10.1901/jaba.1978.11-203

https://doi.org/10.1901/jaba.1978.11-203

• Improving Compliance in Primary School Students with Autism Spectrum Disorder

Abstract
Introduction
Methods
Recruitment and Selection
Participants
Settings
Materials
Criteria Checklist
Event Recording form for Compliance
Momentary Time Sample Recording form for On-Task Behavior
Story: Following Instructions
Role-Play Situations
Self-Monitoring Sheet
Treatment Fidelity Checklist
Behavior Intervention Rating Scale (BIRS)
Children’s Intervention Rating Profile-Adapted Version (CIRP-A)
Independent and Dependent Variables
Independent Variable
Dependent Variable
Experimental Design
Data Collection
Observation Procedure
Inter-observer Agreement
Treatment Fidelity
Procedures
Preliminary Observations
Baseline Phase
Intervention Phase
Part 1: Training
Part 2: Self-Monitoring
Fading Phase
Follow-up
Social Validity
Data Analysis
Results
Participant 1: Sean
Math
English
English Support
Participant 2: John
Reading
Writing
Numeracy
Effectiveness of Intervention
Participant 1: Sean
Math
English
English Support
Participant 2: John
Reading
Writing
Numeracy

Social Validity
Participant 1: Sean
Participant 2: John

Discussion
Limitations
Theoretical Implication
Future Direction
Conclusion
References

Online Worksheet – Five Sources Need to be Listed

Source:

Source:

Source:
Source:

Source:

Online Worksheet – Five Sources Need to be Listed

Laura LLedo Rodriguez

Source: CAU Library

Maccagnano, Ann. (2007). Identifying & enhancing the strengths of gifted learners, K-8: easy-to-use activities and lessons. Corwin Press. ISBN: 9781412951982

https://albizu.ent.sirsi.net/client/en_US/miami/search/results?qu=gifted+students&te=

Source: Google Scholar

Bartholomew, J.B., Errisuriz, L.V., Grieco, L.A., Jowers, E.M. (2016) Phisically Active vs. sedentary academic lessons: A dose response study for elementary student time on task. ELSEVIER, vol.89, pp. 98-103.

https://doi.org/10.1016/j.ypmed.2016.05.021

Source: ERIC

Park, S. (2017) Analysis of Time- on- Task, Behavior Experiences, and Performance in Two Online Courses with Different Authentic Learning Tasks. International Review of Research in Open and distributed Learning, vol. 48, no. 2, pp. 213- 233.

https://eric.ed.gov/?q=time+on+task&id=EJ1138775

Source: FIT Library

Fovet, F. (2007) Using Distance Learning Electronic Tools within the Class to Engage ADHD Students: a Key to Inclusion? 37th ASEE/IEEE Frontiers in Education Conference. October 10-13, 2007, F3D-15 -F3D-20. https://ieeexplore-ieee-org.portal.lib.fit.edu/stamp/stamp.jsp?tp=&arnumber=4417842

Source: JABA

Cook, A., Bradley- Johnson, S., & Johnson, C.M (2014). Effects of White Noise on Off-task Behavior and Academic Responding for Children with ADHD. Journal of Applied Behavior Analysis, vol. 47, pp. 160-174. doi: 10.1002/jaba.79

6

Websites, Books, Independent Studies: 6 Journal Articles Summarized Here

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Fill out both areas for 1 Article on each page (6 pages)

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Arbelo, F. (2016). Pre-entry doctoral admission variables and retention at a Hispanic Serving
Institution. International Journal of Doctoral Education, 11, 269 – 284.
http://www.informingscience.org/Publications/3545

Academic Journal Articles:

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Citation here

George, K. (2016). Evaluating the effects of formal corrective feedback on off-task/on-task behavior of mild intellectually disabled students: an action research study (Thesis doctoral, Capella University).

https://search-proquest-com.ucamia.cobimet4.org/docview/1767788724

			Selection				Explanation
	Source: Primary or Secondary		Primary
	Information Classification: (Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)		Research findings
			How and why is this information pertinent to your selected topic?		The goal of the study looked to identify a potential strategy for addressing the behavioral deficiencies commonly displayed by students classified as mild intellectually disabled as well as any other student determined to have behavioral issues within the classroom setting. Specifically, the study determines if formal corrective feedback influences on the off-task/on-task behavior of mild intellectually disabled students. With this information, we know about treatment choices (strategies) that we can use to change the task refusal behaviors of a student with special needs and increase his compliance with activities and demands.

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)
George, K. (2016). Evaluating the effects of formal corrective feedback on off-task/on-task behavior of mild intellectually disabled students: an action research study (Thesis doctoral, Capella University). https://search-proquest-com.ucamia.cobimet4.org/docview/1767788724

Selection

Explanation

How and why is this information pertinent to your selected topic?

The goal of the study looked to identify a potential strategy for addressing the behavioral deficiencies commonly displayed by students classified as mild intellectually disabled as well as any other student determined to have behavioral issues within the classroom setting. Specifically, the study determines if formal corrective feedback influences on the off-task/on-task behavior of mild intellectually disabled students. With this information, we know about treatment choices (strategies) that we can use to change the task refusal behaviors of a student with special needs and increase his compliance with activities and demands.

	Issues / Topics Covered	Formal corrective feedback, off-task behaviors, on-task behaviors, specific types of off-task behavior.
	Author(s):	George, Kevin
	Research Question(s) addressed:	RQ1: Is there a significant difference between the frequency of off-task behaviors when formal corrective feedback is not applied and the frequency of off-task behaviors when formal corrective feedback is applied? RQ2: Is there a significant difference between the frequency of on-task behaviors when formal corrective feedback is not applied and the frequency of on-task behaviors when formal corrective feedback is applied? RQ3: Does formal corrective feedback have a stronger or weaker effect on specific types of off-task behavior?
	Research Subjects: (pre-K, 9th graders, elementary school students, etc.)	Fifteen tenth graders classified as Mild Intellectually Disabled (MID).
Research setting: ( Public school , 3rd grade class, Charter school, adult learning center, etc.)	Public school
	Methodology:		Mixed research methodology
	Findings:	The study’s results revealed that formal corrective feedback had a significant effect on the off-task/on-task behavior of students classified as being mild intellectually disabled. In this study, the effect appears to translate into a significant decrease in off-task behavior and a significant increase in on-task behavior for all students. This study reduced students’ off-task behavior and improved their on-task behavior through improving their understanding of what they were doing wrong behaviorally in the classroom.
	Conclusions:	Formal corrective feedback effects students’ off-task behavior as well as students’ on-task behavior. The behavioral frequency recordings collected revealed a reduction in off-task behavior and an increase in on-task behavior during the phase of the study in which formal corrective feedback was provided to each student. Formal corrective feedback appeared to have a strong effect on the off-task behaviors of daydreaming/work delay, preoccupied with object or task, laughing out, talking out and sleeping. Formal corrective feedback appeared to have a weak effect on the off-task behavior of improperly seated/out of seat.
	Special Circumstances/Limitations:	The number of participants available (fifteen students) limits the amount of data available for collection and establishment of more definitive results. The exposure of the participants to other teachers and classroom settings in addition to the setting of the study (social science class) could have impacted the study significantly. For consistency purposes, the classroom teacher made all observations. Due to the fact that the one teacher conducted all observations, accuracy may be an issue.
	Future Implications:	The knowledge to be gained from the action research conducted as a part of this study will extend the current understanding of the effect of corrective feedback on the off-task/on-task behavior of students categorized as MID. This extended knowledge will benefit teachers of all students, and specifically those working with students classified as MID. This small-scale study that generated significant findings may lead to the same study being conducted on a larger scale.

2nd Article

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)

Stahr, B., Cushing, D., Lane, K., & Fox, J. (2006). Efficacy of a Function-Based Intervention in Decreasing Off-Task Behavior Exhibited by a Student With ADHD. Journal of Positive Behavior Interventions, 8 (4), 201-211. https://search-proquest-com.ucamia.cobimet4.org/docview/218800369/B5ECD86EF1E142C2PQ/1?accountid=44128

Selection

Explanation

Source: Primary or Secondary

Primary

Information Classification:

(Self-contained study/ Research findings / Professional Association/ Unanalyzed Data / Compiled Statistics, etc.)

Research findings

How and why is this information pertinent to your selected topic?

The study examined the effects of a function-based intervention implemented with a student, Shawn, who had attention-deficit/hyperactivity disorder, internalizing behavioral problems, and a speech and language impairment.  The function-based intervention included a communication system, a self-monitoring component, and extinction. Through this study, we know about interventions that are effective in reduce off-task behaviors of a child with special needs. Therefore, we can use those interventions in our research.

Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)

Citation here:

Selection

Explanation

Issues / Topics Covered

Author(s):

Research Question(s) addressed:

Research Subjects: (pre-K, 9th graders, elementary school students, etc.)

Methodology:

Mixed research methodology

Findings:

Conclusions:

Special Circumstances/Limitations:

Future Implications:

How and why is this information pertinent to your selected topic?

The study examined the effects of a function-based intervention implemented with a student, Shawn, who had attention-deficit/hyperactivity disorder, internalizing behavioral problems, and a speech and language impairment.  The function-based intervention included a communication system, a self-monitoring component, and extinction. Through this study, we know about interventions that are effective in reduce off-task behaviors of a child with special needs. Therefore, we can use those interventions in our research.

function-based intervention, off-task behavior
Stahr, Brenna Cushing, Danielle Lane, Kathleen Fox, James
The research questions were not found in the article.
a 9-year-old African American that received special education services under the category of other health impaired in a fourth-grade classroom fourth-grade classroom.
Research setting: (Public school, 3rd grade class, Charter school, adult learning center, etc.)	A self-contained school serving students with emotional and behavioral problems.
Functional assessment data indicated that Shawn’s off-task behavior was maintained by attention (positive reinforcement) and escape from tasks (negative reinforcement). A function-based intervention including a communication system, a self-monitoring component, and extinction resulted in improvements in Shawn’s behavior. The classroom teachers and Shawn rated the intervention favorably.
Results of descriptive functional assessment procedures indicated that Shawn’s off-task behavior was maintained by both attention (positive reinforcement) and escape (negative reinforcement). An intervention package including a communication system, a self-monitoring component, and extinction was designed to meet both these functions, in addition to addressing his anxiety and speech and language problems. A multiple-baseline design with a withdrawal component indicated that the intervention was effective in increasing on-task behavior in language arts and math. When the intervention was introduced in both settings, on-task behavior increased to twice that of baseline in language and five times that of baseline in math. Despite the limited data on implementation and the variable implementation, student outcome data suggest that the intervention met the intended objective of increasing Shawn’s on-task behavior. Social validity data also indicate that the intervention was generally acceptable to the therapist, lead teacher, paraeducator, and Shawn.
Only a comparatively small amount of the treatment integrity data was collected, thereby preventing a more precise estimate of the extent to which all components of the intervention were implemented as designed.  Data were collected using partial-interval recording, which may have overestimated the level of engagement. A more conservative approach would have been to assess engagement using a whole interval recording procedure.  Although on-task behavior improved in math, the level of academic engagement was still below 80%, the average academic engagement level of typically developing students. Therefore, although levels of engagement improved and the participants viewed the intervention as socially valid, there was still room for improvement in math.  Although results suggest that the intervention was associated with higher levels of task engagement, this study did not assess student achievement.
This study contributes to the literature indicating the effectiveness of a specific intervention package based on the assessed function of a student’s challenging behavior. In that sense it lends further validation to the effectiveness of interventions based on careful, systematic functional behavior assessments. Also, this study further contributes to the empirical base of techniques effective with ADHD students implemented by natural environment agents under naturalistic classroom conditions, specifically addressing this issue in a student with multiple disabilities (ADHD, anxiety, and speech and language delay). As in this study, future behavioral intervention research can be substantially enhanced by evaluating interventions within both traditionally defined subject parameters (i.e., DSM diagnoses of behavior disorders) and more function-based assessment schemata (i.e., subjects whose behavior function is determined through systematic behavioral assessment) that are then matched to intervention components.