Incorporating Special Interests Into Task Analyses and Story Problems

Incorporating Special Interests into Task Analyses and Story Problems

to Increase Accurate Responding for a Child with Autism.

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the Graduate School of The Ohio State University

By: Bobby W. Huffman

Graduate Program in Educational Studies

The Ohio State University

2017

Master’s Examination Committee:

Dr. Helen Malone - Advisor

Dr. Mathew Brock

Copyrighted by

Bobby W. Huffman

2017

Abstract

According to the DSM-V, one common characteristic of children with autism spectrum disorder (ASD) is the frequent engagement in repetitive and restricted interests. Over time, the continuous fixation on these restricted interests may impede on valuable learning opportunities throughout the school day. The purpose of this study was to investigate the use of incorporating restricted interests (herein after referred as “special interests”) into mathematical story problems and the task analyses used during instruction to increase the overall accuracy and decrease interresponse time (IRT) between the steps of the task analyses for an elementary school age girl with ASD. A multiple baseline design across mathematical operations (addition, subtraction, and multiplication) was employed to examine the effectiveness of this intervention. The results demonstrate that incorporating the special interests into the story problems and task analyses significantly increased the participant’s accuracy, however, it did not decrease her IRT. The participant’s mother and paraprofessional reported they would implement this type of intervention in the future. Implications and suggestions for future research are discussed.

Acknowledgments

Special thanks to Dr. Helen Malone and Mary Barczak for their time spent providing guidance to support my research and the revisions to this paper. I also owe a great deal of appreciation to Dr. Diane Sainato who inspired me to become interested in behavior analysis and always pushed me to attend conferences, make presentations, and conduct research. Last but not least, I would like to thank my wife for all the support she gave me and the extra work she endured with our three children so I could pursue my efforts in graduate school.

iii

Vita

June 2001 ...... Unioto High School

2008 ...... B.A. Early Childhood Education,

Ohio University

2012-Present ...... Graduate Student, Applied Behavior

Analysis and Special Education,

The Ohio State University

2017 ...... Expected M.A. Applied Behavior Analysis

and Special Education, The Ohio State

University

Publications

Huffman, R. W., Sainato, D. M., & Curiel, E. S. (2016). Correspondence training using special interests to increase compliance during transitions: An emerging technology. Behavior Analysis in Practice, 9, 25–33. doi: 10.1007/s40617-015-0100-1

Fields of Study

Major Field: Educational Studies

Table of Contents

Abstract ...... ii

Acknowledgments ...... iii

Vita ...... iv

Publications ...... iv

Fields of Study ...... iv

Table of Contents ...... v

List of Tables ...... viii

List of Figures ...... ix

Chapter 1: Background and Literature Review ...... 1

Chapter 2: Method ...... 7

Participant and Setting ...... 7

Materials ...... 8

Dependent Variables and Data Collection Materials ...... 10

Research Design ...... 11

Experimental Conditions ...... 12

Pre-baseline ...... 12

Baseline ...... 13 v

Incorporating Special Interests ...... 14

Maintenance Probes ...... 14

Inter-observer Agreement and Procedural Fidelity ...... 15

Social Validity ...... 17

Chapter 3: Results ...... 18

Mean IRT Assessment ...... 18

MSWO ...... 18

Baseline ...... 18

Incorporation Special Interests ...... 19

Maintenance Probes ...... 22

Social Validity ...... 26

Chapter 4: Discussion ...... 24

Implications for Classroom Practice ...... 26

Limitations and Future Research ...... 27

Conclusion ...... 29

References ...... 30

APPENDIX A: BASELINE ADDITION/SUBTRACTION TASK ANALYSIS ...... 38

APPENDIX B: BASELINE MULTIPLICATION TASK ANALYSIS ...... 40

APPENDIX C: ADDITION TASK ANALYSIS WITH SPECIAL INTERESTS ...... 42

APPENDIX D: SUBTRACTION TASK ANALYSIS WITH SPECIAL INTERESTS .. 44

APPENDIX E: STORY PROBLEM EXEMPLARS ...... 46

APPENDIX F: ADDITION AND SUBTRACTION DATA SHEET ...... 48

APPENDIX G: MULTIPLICATION DATA SHEET ...... 50

APPENDIX H: MSWO DATA SHEET ...... 52

APPENDIX I: PROCEDURAL FIDELITY CHECKLIST ...... 54

vii

List of Tables

Table 1. Social validity results ...... 17

Table 2. The mean and range of the percentage of correct responding per task analysis . 19

Table 3. The mean and range of total IRT per task analysis ...... 23

viii

List of Figures

Figure 1. Task analyses without and with special interests for addition ...... 9

Figure 2. Percentage of correct responding per task analysis ...... 20

Figure 3. Total IRT per session ...... 22

Chapter 1: Background and Review of the Literature

According to the Diagnostic and Statistical Manual of Mental Disorders, 5th

Edition (DSM-V; APA, 2013), one diagnostic component for autism spectrum disorder

(ASD) is the frequent engagement in interests that are highly restricted and abnormal with regards to intensity or focus. Researchers have labeled these types of interests as special interests (Attwood, 1998), obsessions (Baker, 1998; Myles & Simpson, 2003), circumscribed interests (Attwood, 2003), and fixations (Happe, 2001), and they have examined the impact of these interests on the everyday lives of individuals with ASD

(Gunn & Butt, 2016). In previous studies, these types of interests have included a wide range of topics such as toilet brushes (Attwood, 1998), photocopiers (Myles & Adreon,

2001), jigsaw puzzles (Carnett et al., 2014), and rock and roll musicians (Huffman,

Sainato, & Curiel, 2016), and they are prominent in approximately 90% of children and adults with ASD (Attwood, 2003). Although neurotypical individuals may share the same interests, individuals with ASD define themselves by their special interests rather than view the given interest or topic as a hobby (Gunn & Butt, 2016; Messiers et al., 2007).

Furthermore, individuals with ASD have reported their special interests help them make sense of the word and provide the stability they need for their overall well-being (Gagnon

& Myles, 2016).

Given the importance of these special interests to individuals with ASD, parents of children with ASD have reported the frequent engagement with these interests often creates challenges, especially when the individual is requested to stop engaging in a given activity that involves the special interest (Klin, Danovitch, Merz & Volkmar, 2007;

Meissers et al., 2007). Another concern reported by parents and educators is the worry that the obsession will grow stronger if the individual with ASD is given frequent access to the special interest (Baker, 2000; Gunn & Butt, 2016). Prior to the understanding of these special interests and the role they play for individuals with ASD, one common intervention goal was to minimize or extinguish the behavior associated with the special interest from the repertoire of the individual with ASD (Gagnon et al., 2016). The rationale for this practice was based on the perception that special interests are characteristics of a deficit that need to be targeted for reduction (Attwood, 2003; Charlop-

Christy & Haymes, 1996; Meissiers et al., 2007). Conversely, current research outcomes have suggested that incorporating special interests into instruction may increase desired behaviors with a concomitant effect of decreasing undesired behaviors (Carnett et al.,

2014; Huffman et al., 2016; Koegel et al., 2010). Therefore, current research examining the effectiveness of incorporating special interests into instruction and/or daily activities has identified this practice as a strength-based model (Gagnon et al., 2016; Gunn & Butt

2016).

Han Asperger, an Australian pediatrician for whom Asperger Syndrome was named, was one of the first professionals to recognize the importance of special interests and the hidden talents exhibited by individuals with high functioning ASD (Asperger,

1944; Gunn & Butt, 2016). Asperger (1944) also indicated that special interests enable these individuals to achieve quite extraordinary levels of performance in a certain area.

Following this perspective that special interests may unlock hidden potential for individuals with ASD, several researchers over the past three decades have confirmed this outcome through various types of intervention models that used special interests.

Incorporating special interests in behavioral research has been used to increase task engagement and academic performance (Charlop-Christy & Haymes, 1998; Huffman et al., 2016; Koegel, Signh, & Koegel 2010; Lanou, Hough, & Powell, 2011; Mancil &

Pearl, 2008; Zein, Solis, Lang, & Kim, 2016), increase joint attention (Kryzak & Jones,

2015; Kryzak, Bauer, Jones, & Sturmey, 2013), reduce challenging behavior and increase compliance (Angel, Nicholson, Watts, & Blum, 2011; Campbell & Tincani, 2011;

Carnett et al., 2014; Charlop-Christy & Haymes, 1996; Huffman et al. 2016; Koegel et al., 2010;), and increase social/play skills (Baker et al. 1998; Baker, 2000; Daubert,

Hornstein, & Tincani, 2015; Jung & Sainato 2015; Keeling et al. 2003; Keogel et al.,

2012; Spencer et al., 2008; Vismara & Lyons, 2016; Watkin et al., 2017).

One intervention that has demonstrated several replications of positive outcomes is the Power Card strategy (Gagnon, 2001). The Power Card strategy is comprised of two components: (a) a scenario written in first person describing how the individual’s special interest character/hero problem solves the given difficult situation, and (b) a 5 x 8 inch notecard that provides three to five strategies by the special interest character about how to be successful in the given problematic situation. Keeling (2003) used the Power Card strategy to teach sportsmanship skills to a 10-year-old girl with autism. Angell (2011)

3 used the Power Card strategy to decrease latency time during transitions for three boys ages 10–11 years old. In more recent studies, Daubert (2014) used the Power Card strategy to increase turn taking skills during board games for two boys ages 9–10 years old. In addition to the Power Card strategy, incorporating special interests has been utilized in several other ways to increase desired behaviors.

Recently, the use of special interests has been incorporated into instructional stimuli and interventions to enhance the value of the intervention for the individual with

ASD. Video modeling is one evidence-based intervention that has been used to increase a wide range of behaviors for individuals with ASD (Hong et al., 2016). Jung and Sainato

(2015) used special interest characters as the models in the videos to increase engagement and social interactions during board games for three kindergarten-age students with ASD.

Furthermore, the effects of the intervention generalized to a novel game. Another type of intervention that has been examined with the addition of special interests is token economies (Carnett et al., 2014; Charlop-Christy et al., 1998). Charlop-Christy (1998) used special interests as the tokens (e.g., used trains instead of stars) to increase on-task behavior and decrease challenging behavior for three elementary–age children with autism. The condition with special interests as the tokens resulted in higher levels of desired responding when compared to regular tokens. In addition, Carnett (2014) also compared the use of special interests as the token versus regular tokens to increase on- task behavior and decrease challenging behavior for an elementary-age boy with ASD.

The outcomes of this study also resulted in higher levels of on-task behavior and a

4 reduction in challenging behavior when the token was a special interest item rather than an unrelated token.

Although research outcomes over the past 30 years have provided favorable outcomes for the use of special interests, the following limitations should be mentioned.

First, a majority of the studies that enhanced evidence-based interventions by incorporating special interests did not examine the effects of the intervention without special interests (Angel et al., 2011; Campbell et al., 2011; Daubert et al., 2015; Huffman et al., 2016; Jung et al., 2015; Keeling et al., 2003; Ohtake, 2015; Spencer et al., 2008).

Therefore, it is unknown if the evidenced-based intervention alone would have been just as effective as when the special interests were embedded into the intervention. For example, Jung (2015) and Ohtake (2015) both incorporated special interests into video modeling, however the effects of video modeling alone were not investigated in either study. The Power Card strategy is another special interest intervention that has resulted in desired outcomes; nevertheless, the majority of the aforementioned Power Card studies did not compare the effects of using the Power Card strategy to a social narrative without special interests (i.e. social story). Lastly, Huffman (2016) incorporated special interests into a package intervention consisting of correspondence training and a visual task analysis that increased on-task behavior and compliance during transitions for a pre- school-age child with special needs; however, this study did not compare the effects of correspondence training and task analytic picture cues without the use of special interests.

Another common limitation in the special interests literature is the absence of reporting maintenance data (Angell et al., 2011; Boyd et al., 2007; Carnett et al., 2014;

Keeling et al., 2003 Vismara et al., 2016; Zein et al., 2016). Although these studies demonstrated favorable outcomes with the use of special interests, one unknown outcome is whether the desired behaviors would have maintained over time once the special interests were removed. Therefore, one important objective of the present study is to address these two common limitations by comparing the effects of an evidence-based intervention with and without special interests and by examining whether the desired behaviors will maintain once the special interests are removed.

Specifically, this study was conducted to answer the following research questions:

1. Will the incorporation of special interests into a task analysis and story problems

increase the participant’s response accuracy?

2. Will the incorporation of special interests decrease the interresponse time between

the steps of the task analysis?

3. Will the effects of the intervention maintain once the special interests are removed

from the intervention?

4. Will the participant’s parents and professionals who work with her agree to use

the intervention if the intervention results in desired outcomes?

Chapter 2: Method

Participant and Setting

The participant in this study was a Caucasian girl named Sarah with ASD who was 11 years old and in the fifth grade. Sarah received specialized instruction in mathematics, reading comprehension, written expression, executive functioning, and social skills that was facilitated by her individualized education program (IEP). Sarah participated in her classroom with the assistance of a paraprofessional during non- academic activities, however her instruction that targeted her IEP goals was provided exclusively in the special education setting. A review of Sarah’s IEP and daily schedule revealed that Sarah was working on solving story problems with fractions using addition, subtraction, and multiplication. Sarah’s present level of performance data from her IEP illustrated that Sarah was able to add, subtract, and multiply fractions in isolation, however, when these concepts were incorporated in a story problem, Sarah needed continuous prompting to solve the story problem.

Sarah was selected for this study due to her perseverative interest in Looney

Tunes that consisted of frequently scripting Looney Tune characters and acting out scenes from various Looney Tunes episodes. Anecdotal data that was reported in her IEP stated these perseverative behaviors occurred continuously throughout the school day and were highly disruptive to her learning. Therefore, Looney Tunes characters were

7 incorporated into each task analysis and story problem used during each intervention condition. This study was conducted during regular school day hours and occurred in a quiet space of the special education resource room.

Materials

A visual task analysis was created for each operation to teach Sarah the steps for solving each type of word problem. During baseline and maintenance, the visual task analysis depicted all the steps to solve the word problem displayed in written format due to Sarah’s strong reading capabilities. During intervention, the instructional steps in the task analysis were phrased the same, however the intervention task analysis differed in the following ways: (a) titled Looney Tunes Work Schedule, (b) each step of the task analysis had a Looney Tunes character beside it to portray the Looney Tunes character giving the instruction, and (c) additional Looney Tunes thematic phrases added to the end of some of the steps (e.g., Elmer Fudd saying to “find the common denominator so I can go rabbit hunting”). In this case, adding “rabbit hunting” was added to increase the likelihood of attending to the task analysis without changing the actual instructional content (Baker, 2000). Figure 1 depicts one of the Looney Tunes task analyses used during intervention for multiplication.

In addition to the modifications made to each task analysis used during the intervention conditions by incorporating Looney Tunes characters, the story problems were also modified by substituting the original characters from the story problems with

Sarah’s favorite Looney Tunes characters. The following characteristics of the story problems remained constant throughout each experimental condition in the study: (a) 4–5

8 sentences, (b) characters, (c) three fractions with one of the fractions as a distractor for

Sarah to disregard, and (d) key words that trigger the given operation for the story problem. During baseline and maintenance, the characters in the story problems were names of people that were not related to her special interests (e.g., Billy, Kelsey). During intervention, the characters in each word problem were Looney Tunes characters. All the

Looney Tunes characters that were incorporated into the intervention were her most highly preferred from the results of the Multiple Stimulus Without Replacement preference assessment (MSWO) (see pre-baseline) conducted prior to the start of intervention. Additional materials used throughout the study included a Flip Recording

Camera, a MacBook Air Computer, and special-interest-based and non-special interest- based reinforcers for task completion.

1. Write out all the fractions and what they are (you can just write first letter)

Fraction What Fraction What Fraction What Write out all the fractions and what they are under each fraction

Fraction Fraction 2. Cross out any fractions you don’t need. Fraction Common Denominators Cross out the Circle the Operation Circle One Write out the fraction 3. Circle the operation to use: garages and circle + − × ÷ fractions you to use Yes = solve numerators What What What don't need doc the least common denominator 4. Common denominators:(Circle One) Yes, then just solve numerators No = do what porky says to do No, go to number 6

6. Write out the fraction garages and circle the least common denominators

7. Write the garage number beside both new-circled denominators

8. Rewrite the new fraction lines with the operation and = sign ( --- ( ) --- = ---) Multiply the garage number to their Rewrite the new numerators and 9. Write the new-circled denominator under each three fraction lines Use the operation Write the new fraction lines with write those 10. Multiply the garage number to their numerators and write those numerators garage number the operation you Write the new numerators on your you circled from on your new fraction lines beside both new circled with Daffy! circled denominator new fraction lines Daffy to solve the You did your Looney Tunes ------( ) ------= ------circled denominators under each three numerators. ( ) = Schedule! fraction lines ( ) = 11. Use the operation you circled (from number 3) to solve the numerators

12. All Done Do your Work Schedule Here Do#Work#Here# !

Figure 1. Task analyses without and with special interests for addition.

Dependent Variables and Data Collection

The dependent variables used for this study were (a) accuracy: the percentage of correctly completed steps of the task analysis and (b) interresponse time (IRT): the total amount of time that elapsed between consecutive responses within the task analysis

(Cooper, Heron, & Heward, 2007). Throughout the study, accuracy was the primary measure and IRT was the secondary measure. For accuracy, a correct response was defined as any response that matched the exact instructional step of the task analysis. The procedure for measuring the percentage of correct responses per task analysis consisted of giving Sarah a word problem with the task analysis and then recording each step of the task analysis as correct or incorrect. The formula for calculating the percentage of correct responding per task analysis was as follows: correct responses divided by the total number of steps in the task analysis multiplied by 100.

IRT was measured by recording the time in seconds that elapsed between two consecutive responses within the task analysis. Once the experimenters recorded all the

IRT opportunities for the given session, the total sum was calculated for each experimental session. Each session was recorded with a camera, then the video was uploaded to a computer for the experimenter to calculate IRT. During the IRT recording process, if Sarah’s IRT was 1 s or less, that IRT was recorded as 0. Furthermore, if

Sarah’s IRT reached 20 s, and she was engaging in a behavior that was irrelevant to the given task analysis step (e.g., singing to herself), a verbal prompt was delivered to remind

Sarah to follow her schedule. Once the verbal prompt was delivered, IRT was calculated by recording the time between the end of the verbal prompt and the first initial movement

10 to the next step in the task analysis. This prompting protocol remained constant throughout the study and was established during pre-baseline (see below). Each step of the task analysis required a writing component (i.e. circling, calculating, writing numbers), therefore, each IRT opportunity was measured by recording the time between her pencil coming off the paper to the first initial movement of the next written response.

For example, if the first response was to write the fraction 2/5 and the next response was to write the fraction 4/7, the recording of IRT started after Sarah wrote the denominator of 5 and stopped as soon as she initiated the writing of the number 4 for the next fraction.

However, if Sarah was writing the fraction 2/5 and she wrote the number 2 with the fraction line and then stopped (i.e., pencil coming off the paper and looking around the room), IRT was recorded from that point until she wrote the denominator 5. This scenario was hypothesized as a possible outcome; therefore, the recording of the pencil on and off the paper ensured a reliable and consistent data collection process throughout the study.

Research Design

A multiple baseline design across mathematical operations was used to examine the effect of incorporating special interests into three task analyses and sets of story problems. The three operations used were addition, subtraction, and multiplication of two fractions within the context of story problems. There were four conditions for this study: pre-baseline, baseline, intervention incorporating special interests, and maintenance.

Experimental Conditions

Pre-baseline. The purpose of the pre-baseline condition was twofold: (a) to determine the average IRT to establish a fixed-time prompting schedule for the experimenter to use during all subsequent conditions (Huffman et al., 2016) and (b) to conduct a preference assessment to identify the most preferred Looney Tunes characters to be incorporated into the intervention. The process for collecting the IRT data to determine a fixed time prompting schedule was conducted in three pre-baseline sessions.

Each session consisted of giving Sarah three story problems that matched the skill components of her IEP to complete while the lead experimenter collected IRT data throughout the session. During pre-baseline, the lead experimenter did not provide any specialized instruction for the story problems. Once all three pre-baseline sessions were completed, the mean of all IRT data points was calculated by: dividing the sum of all interresponse times by the total number of interresponse time occasions recorded during the pre-baseline condition (Cooper, Heron, & Heward, 2007). This calculated IRT mean determined how much time would elapse before the experimenter initiated the fixed- prompting schedule. The fixed-time prompting schedule consisted of the following three step sequence once the IRT limit (as determined by the results of the aforementioned assessment) had been reached: (a) verbal prompt, (b) if the verbal prompt did not evoke the initiation of the task within the fixed IRT limit, then a verbal/gestural prompt was delivered, and (c) if the aforementioned prompts did not evoke the initiation of the task within the next IRT limit, then a partial physical prompt (hand-over-hand) was used to support Sarah.

The second component of the pre-baseline condition was to determine the most highly preferred Looney Tunes characters through the use of a Multiple Stimulus Without

Replacement preference assessment (MSWO) (DeLeon & Iwata, 1996). Prior to the assessment, the lead experimenter asked Sarah to identify her top 10 Looney Tunes characters. During this interview, pictures of Looney Tunes characters were available to assist her in the event she needed them. The purpose of this interview was to determine

10 Looney Tunes characters that would be used in the MSWO preference assessment.

The MSWO was conducted as follows: (a) 10 Looney Tunes characters were quasi- randomly sequenced in a straight line on the table, each approximately 2 inches apart, (b)

Sarah was asked to “pick one” and then was allowed to access and talk about the character for 30 s, (c) prior to beginning the next trial, the sequence of the remaining

Looney Tunes characters was rotated by taking the Looney Tunes character at the left end of the line and moving it to the right end, and (d) each picture was shifted to ensure they were approximately 2 inches apart before each trial. This process continued until all

Looney Tunes characters were selected.

Baseline. The experimenter created three task analyses (one for each operation) to teach Sarah the steps to completing each story problem. The story problems were created from a generator from math-aids.com that allows for controlling for specific difficulty levels to match the needs of the given student. Throughout baseline, each session consisted of the following four steps. First, the experimenter read aloud the story problem and modeled the steps depicted in the task analysis. Second, the experimenter read aloud the story problem and allowed Sarah to use the task analysis to complete the

13 story problem while the experimenter provided least–to– most prompts as needed. Next, the experimenter read aloud the story problem and requested Sarah to use the task analysis to complete the story problem while the experimenter employed the 20 s fixed– prompting schedule. Experimenter prompts during the test was contingent on the passage of 20 s of off-task behavior. For example, if Sarah completed the first two steps of the task analysis and then started engaging in non-related behaviors (e.g., scripting Looney

Tune characters while looking away), least–to–most prompts (verbal–gestural–physical) were delivered on an FI20 schedule. Finally, a reinforcer menu was available contingent on completing the task analysis that consisted of a 7-min. ipad activity, 7 minutes of swinging in the motor room, or 7–min. computer activity.

Incorporating special interests. The procedures used in intervention were identical to the procedures used in baseline with the exception of the incorporation of

Looney Tunes characters embedded into the task analyses, story problems, and reinforcer activities contingent on task completion. Furthermore, the experimenter employed the same model-lead-test instructional sequence and fixed-time prompting schedule that was used during baseline. These procedures remained constant throughout each experimental condition to ensure the only change was the incorporation of special interests to allow for a valid comparison to the procedures with and without the incorporation of Looney

Tunes.

Maintenance probes. The procedures used throughout maintenance were identical to the procedures used in baseline. All Looney Tune characters were removed from the story problems and each task analysis. The same teaching procedures and

14 reinforcement contingencies were also identical to baseline. Maintenance data were collected for two consecutive days and then every other day contingent on data stability.

Interobserver Agreement and Procedural Fidelity

Interobserver agreement (IOA) was collected independently for 30% of each experimental condition for both dependent measures of this study (i.e., accuracy and

IRT). The second observer was a doctoral student in the program of Applied Behavior

Analysis. Prior to IOA data collection, the lead experimenter trained the second observer by operationally defining the response measurements and then conducting IOA training sessions. These sessions consisted of the lead experimenter recording the participant completing a story problem and then both lead experimenter and second observer independently viewing the video while collecting the IOA data. The criterion for starting

IOA data collection was three consecutive sessions with 95% or higher agreements between the lead experimenter and second observer. Agreements for accuracy were defined as both observers recording each response as correct or incorrect of the task analysis per story problem. The formula for calculating IOA for accuracy was as follows: dividing the number of agreements by the total number agreements plus disagreements and then multiplying by a 100 (Cooper et al., 2007).

Mean duration per occurrence was the data collection method for calculating IOA for IRT (Cooper et al. 2007). This procedure consisted of the following four steps: (a) divide the shorter of the two durations recorded by both observers by the longer duration to obtain a percentage for each IOA data point, (b) add all the individual percentages from the first step, (c) divide the sum of all the IOA’s per occurrence by the total number

15 of IOA data points collected by both observers, and (d) multiply the quotient by 100 and round to the nearest whole number. Cooper et al. (2007) reported this method as the most valid procedure for collecting IOA data for IRT.

The mean and range for the percentage of accurate responding per session agreements was 100% across all three experimental conditions. The mean and range for total IRT agreements across all three experimental conditions was as follows: Baseline =

97% (94–100%), Special Interests = 98% (96–100%) and Maintenance = 98% (96 –

100%).

To examine procedural fidelity, a checklist was completed following each session per experimental condition. The checklist consisted of seven items that outlined the experimenter’s behaviors throughout each condition. Given that the only change between baseline/maintenance and intervention was the incorporation of the special interests, the components of each procedural checklist remained the same for all three experimental conditions. Procedural fidelity was examined by dividing the total number of checked items by the total number of items on the checklist and multiplying by 100. For example, if the experimenter exhibited 6 out of the 7 of the behaviors on the checklist, then 6 was divided by 7 and then multiplied by 100 to get the percentage of correct procedural behaviors for that given experimental session.

During baseline, the results were as follows: multiplication = 98%; subtraction =

98%; and addition = 97%. The procedural fidelity results during intervention was the following: multiplication = 100%; subtraction = 98%; and addition = 100%. Lastly, during maintenance, procedural fidelity was 100% across all three mathematical

16 operations. The range for all experimental conditions was 86% (6 out of 7) to 100% of correctly exhibited experimenter behaviors.

Social Validity

Sarah’s mother and a paraprofessional who worked with Sarah each day at school completed the questionnaire to assess their understanding, acceptance, and desire to implement this type of intervention in the future. Prior to completing the questionnaire, the lead experimenter met with Sarah’s mother and paraprofessional to review the data from the study. During this time, any questions regarding the procedures and outcomes of the study were answered to ensure both respondents had an adequate base of understanding before completing the questionnaire. Next, the questionnaire was given to both participants and requested to complete at a later time to minimize any possible confounds or reactive effects of completing the questionnaire in the presence of the lead experimenter. The values of the 7-point scale were strongly disagree, disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree, and strongly agree.

Table 1. Social validity results.

Item Mother Para-Professional Incorporating the special interests into the 7 7 activities increased the student’s overall accuracy with each task. Incorporating the special interests into the 6 6 activities increased the student’s overall on-task behavior with each task. Incorporating the special interests into the 4 3 activities increased the student’s overall time to initiate each step of the task analysis. I understood the procedures used in this study 7 7 with incorporating special interests into daily tasks. The incorporation of special interests into daily 6 7 activities is an easy intervention to implement. I will use this intervention or components of this 6 7 intervention in the future.

Chapter 3. Results

Mean IRT assessment

The purpose of this assessment was to determine the total mean IRT that was used for the fixed-time prompting scheduling. The mean IRT across three sessions was 20 s

(range: 15–24 s). Therefore, a fixed 20-s interval was used as the time that elicited prompting throughout the study.

MSWO

The MSWO was administered to identify the most preferred Looney Tunes characters used during intervention. The most–to–least preferred Looney Tunes characters were Bugs Bunny, Tweety Bird, Slyvester the Cat, Daffy Ducky, Porky Pig,

Road Runner, Elmer Fudd, Tasmanian Devil, Coyote, and Foghorn Leghorn.

Baseline

Figure 2 and Table 1 depict the percentage of accurate responding for each mathematical operation per experimental condition. During baseline, Sarah correctly answered 40% (range: 31–44%) of the multiplication story problems, 40% (range: 33–

52%) of the subtraction story problems, and 43% (range: 24–52%) of the addition problems. Figure 3 and table 2 depict the total interresponse time (IRT) per session for each experimental condition. Sarah’s IRT for the multiplication story problems was 48 s

(range: 30–87 s), 42 s (range: 18–99 s) for subtraction story problems, and 39 s (range: 4–

109 s) for addition.

Table 2. The mean and range of the percentage of correct responding per task analysis.

Operation Sessions Baseline SI Main

x 29 40 94 100 (31–44) (75–100) - 31 40 95 93 (33–52) (86–100) (86–100) + 34 43 98 99 (24–52) (86–100) (95–100) ______Note. X = Multiplication; + = Addition; - = Subtraction; SI = Special Interests; Main = Maintenance

Incorporating special Interests

Figure 2 and Table 1 depict the accuracy data per condition for each mathematical operation. The incorporation of special interests more than doubled Sarah’s overall percentage of accurate responding across all three mathematical operations. When special interests were added to the multiplication story problems, Sarah’s correct responding increased, and the mean percent correct was 94% (range: 75–100%). Sarah’s accuracy increased from 38% to 81% from the last baseline session to the first intervention session and continued an upward trend to 100% by the third data point. From this point, her accuracy remained stable in the 80% to 100% range minus one data point that decreased to 75%; however, her accuracy increased back up to 100% for the last two intervention sessions. When special interests were added to the subtraction story problems, the mean percent correct was 95% (range: 86–100%). Sarah’s accuracy increased from 43% to 100% from the last baseline session to the first intervention

19 session and remained stable in the 86% to 100% range for the duration of this condition.

Finally, when special interests were added to the addition story problems, the mean percent correct was 98% (range: 86–100%). Sarah’s accuracy increased from 38% to

100% from the last baseline session to the first intervention session and remained stable in the 86% to 100% for the duration of this condition.

Baseline !!!!!!Incorporating Special Interests Maintenance 100 90 80 70 60 50 40 30

20 10 Muliplication 0 ! 100 90 80 70 60 50 40 30

20 !!!! 10 !!Subtraction 0 ! 100 90 80 70 60 50

Percentageof Correct Responding Per Task AnalysisFor Each MathematicalOperation 40 30 20 10 Addition 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 ! Sessions

Fig. 4. Percentage of correct responding per task analysis

Figure 2. Percentage of correct responding per task analysis

Although a clear functional relation was demonstrated with accuracy, this effect was not demonstrated with IRT. Specifically, Sarah’s mean IRT for all three mathematical operations was higher during intervention. Figure 3 and Table 2 depict the

IRT data per session for each experimental condition. When special interests were added to the multiplication story problems, Sarah’s IRT significantly increased from 42 s to 118 s from the last baseline session to the first intervention session. However, the following three data points consisted of a descending trend down to 32 s. From this point, Sarah’s

IRT data was highly variable for the duration of the multiplication intervention sessions.

The mean IRT during multiplication was 61 s (range: 23–118 s) When special interests were added to the subtraction story problems, an ascending trend occurred for the first four data points before decreasing down to 22 s. From this point, an ascending data pattern persisted throughout the duration of the subtraction intervention condition. The mean IRT during subtraction was 78 s (range: 21–198 s). Finally, when special interests were added to the addition story problems, Sarah’s IRT increased from 4 s to 53 s from the last baseline session to the first intervention session and increased to 102 s by the fourth session. The following three data points descended down to 27 s before increasing up 93 s, however, the last two data points decreased to 35 s and 38 s, respectively. The mean IRT during the addition intervention conditions was 59 s (range: 27–102 s).

Baseline Incorporating!Special!Interests Maintenance 200 180 160 140 120 100 80 60

40 20 0 Multiplication ! 200 180 160 140 120 100 80 60 40 20 Subtraction 0 ! ! 200 180 160 Each SessionOperation PerIRT Total Mathematical (SEC) for 140 120 100 80 60 40 20 0 Addition 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 ! ! Sessions !

Figure 3. Total IRT per session.

Maintenance Probes

The treatment effect of incorporating special interests for accuracy remained stable throughout the maintenance condition for all three mathematical operations. The mean and range for the percentage of accurate responding for multiplication was 100%, for subtraction was 93% (range: 86–100%), and addition was 99% (95–100%). The mean

22 and range of Sarah’s IRT were 29 s (range: 8–56 s) for multiplication, 45 s (range: 32–77 s) for subtraction, and 51 s (range: 37–80 s) for addition.

Table 3. The mean and range of total IRT per task analysis

Operation Sessions Baseline SI Main

x 29 48 61 29 (30–87) (23–118) (8–56) - 31 42 78 45 (18–99) (21–198) (32–77) + 34 39 59 51 (4–109) (27–102) (37–80) ______Note. X = Multiplication; + = Addition; - = Subtraction; SI = Special Interests; Main = Maintenance

Chapter 4: Discussion

This study investigated the effects of incorporating special interest characters into a visual task analysis and story problems to increase accuracy and decrease interresponse time between the steps of the task analysis used for each mathematical operation.

Incorporating special interests increased Sarah’s accuracy with all three operations; however, a functional relation for decreasing her IRT between the steps of the task analysis was never established. Furthermore, Sarah’s accuracy remained stable during all three maintenance sessions that consisted of removing all special interests from the intervention. An additional positive outcome from this study was that Sarah’s mother and paraprofessional agreed the intervention was effective for increasing accuracy and reported an interest in using this intervention model in the future.

The positive outcomes from this study are consistent with previous research findings that used special interests to increase task performance for individuals with ASD or other developmental disabilities that exhibit restricted and/or repetitive behavior patterns (Charlop-Christy et al., 1998; Huffman et al., 2016; Koegel et al., 2010; Lanou et al., 2011; Mancil et al., 2008; Zein et al., 2016). More specifically, the findings are also consistent with previous research outcomes that enhanced evidence-based interventions by incorporating special interests (Carnett et al., 2014; Charlop-Christy et al. 1998;

Huffman et al., 2016; Jung et al., 2015; Ohtake et al., 2015). In addition, one noteworthy 24 component of our study was the comparative analysis of the intervention with and without the incorporation of special interests that has been absent in several of the aforementioned studies. Throughout each experimental condition, the only change to the procedures was the incorporation of special interests, thereby providing a set of experimental procedures that allowed a valid analysis of the dependent measures with and without special interests. Although several of the aforementioned studies enhanced evidence-based interventions by embedding special interests, another common limitation in the special interest literature that was addressed in our study was the assessment of maintenance of the desired behavior change by removing the special interest incorporated into the intervention. For example, Charlop-Christy (1998) and Carnett (2014) both directly compared the effects of a token economy with and without special interests and demonstrated that token economies with special interests were more effective than token economies without special interests. Although both studies directly compared the effects of token economies with and without special interests, neither demonstrated a maintenance outcome consisting of the participants exhibiting the desired behaviors once the special interests were removed.

Conversely, the participant in our study maintained her accuracy across five maintenance probes that consisted of removing all special interests from the task analyses and story problems. One possible reason for this outcome may be explained through the lens of the paired associative learning theory (PAL) (Wang, Wass, & Castles, 2017). PAL is a behavioral phenomenon that occurs when an individual’s response to one stimulus is under the same control as another stimulus through the process of pairing (Wang et al.,

2017). For example, in this study, each mathematical operation was associated with a specific Looney Tunes character during the intervention condition (e.g., Foghorn

Leghorn = subtraction). This phenomenon was overtly noticed during maintenance when

Sarah would often verbalize statements such as, “Oh good it’s Foghorn Leghorn subtraction, I know what to do,” or “I love doing Tweety Bird multiplication!” when she was given the story problems and task analyses without the special interest characters.

Therefore, the pairing of the special interest characters with the given mathematical operation during intervention may have mediated accurate responding during maintenance. This outcome is consistent with previous conclusions that the use of special interests may unlock hidden potential skills for individuals with ASD (Gagnon et al.,

2016; Gunn & Butt, 2016; Myles 2013).

Implications for Classroom Practice

Incorporating the special interest characters into the story problems and visual task analyses used during instruction was a quick modification that required a minimal amount of preparation time. In addition, this intervention did not require any different teaching techniques from the instruction that was used with the visual task analyses and story problems without the special interest characters. The only additional tasks required were the administration of a preference assessment, substituting special interest characters with regular characters in the story problems, and embedding the special interest characters as the “communicators” for each step in the visual task analysis.

Therefore, modifying instructional stimuli by incorporating special interests may be a quick solution for individuals with ASD who exhibit difficulty attending to instruction

26 during various academic activities. The guidelines for implementing this intervention for individuals with special interests are the following:

1. Conduct a preference assessment (MSWO) to identify the special interest(s) that

will be incorporated into the intervention.

2. Develop a task analysis for the given skill and create a visual schedule that

depicts the steps of the task analysis.

3. Incorporate the special interest character(s) into each step of the visual task

analysis as the “communicator” for the given step of the task analysis (i.e. Bugs

Bunny says to multiply the two fractions).

4. If using story problems, substitute the regular characters with the special interest

characters in the story problem.

Once the individual begins to establish stable, accurate responding (i.e., 5 consecutive data points above 80%), administer maintenance probes (removal of special interest) to assess generalization.

Limitations and Future Research

Although the results were positive, there are a number of limitations that can be addressed in future studies. First, the positive results from this study occurred with only one participant, therefore, further replications are warranted to examine the effectiveness of incorporating special interest characters into a task analysis and story problems.

Moreover, special interests were incorporated into the task analyses and story problems, thereby making it difficult to conclude if this combination was necessary to elicit the desired behavior change or if either independent variable in isolation would have resulted

27 in the same outcome. Future research could administer a component analysis to investigate which independent variable or combination is necessary to increase the individual’s accuracy with the steps of the task analysis and story problems. Lastly, incorporating special interests was unsuccessful with establishing a functional relation for the secondary measure (decreasing IRT) of this study, thereby limiting the positive effects of this intervention to only one dependent measure (accuracy). In the future, researchers may be able to address decreasing IRT by establishing an additional reinforcement contingency based on a predetermined IRT criterion per session. For example, researchers could calculate a mean IRT of all previous sessions and then set the

IRT criterion for reinforcement slightly below the calculated IRT mean. Next, a changing criterion design could be used to gradually increase the response requirements by requiring the individual to complete the task analysis with a fixed gradual decrease in total IRT to meet the reinforcement contingency. When using this model, Roane (2007) suggested applying the behavioral economic principle of unit price by increasing the reinforcement magnitude (e.g. minutes of computer time) by the same percentage as the increased response requirement (e.g. decreased total IRT). For example, if the total IRT was decreased by 20% to meet the reinforcement contingency, then a 20% increase would be applied to the reinforcer magnitude to maintain a constant “price” and minimize the likelihood of evoking a ratio strain within the reinforcement contingency.

Researchers may also be able to decrease IRT with the task analysis by adding an additional special interest intervention. In this case, the Power Card strategy (Keeling et al., 2003) could be added that consists of the individual’s special interest character

28 communicating the importance of completing the task analysis without engaging in any unrelated behaviors. Prior to each session, the researcher could read the social narrative and then place the 3x5 instructional notecard next to the individual as reminder of the behavior expectations for the duration of session.

Lastly, social validity is worth noting as a limitation. Although Sarah’s mother and para-professional were pleased with the accuracy outcome, their responses to their agreement of the intervention decreasing IRT was low. Therefore, acceptance of this intervention as being effective was limited to only one dependent measure.

Conclusion

Incorporating special interests resulted in significant increases in accuracy across all three mathematical operations. More importantly, the participant’s accuracy maintained once the special interests were removed. Another highlight to this study was the positive feedback given by the participant’s parents and para-professional. Although future research is merited to support the results from this study, the incorporation of special interests into a task analysis and story problems may facilitate success for individuals with ASD that demonstrate difficulty comprehending a task analysis and story problems in the absence of their special interests.

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0020701

APPENDIX A: BASELINE ADDITION/SUBTRACTION TASK ANALYSIS

1. Write out all the fractions and what they are (you can just write first letter)

Fraction What Fraction What Fraction What

2. Cross out any fractions you don’t need.

3. Circle the operation to use: + − × ÷

4. Common denominators:(Circle One) Yes, then just solve numerators

No, go to number 6

6. Write out the fraction garages and circle the least common denominators

7. Write the garage number beside both new-circled denominators

8. Rewrite the new fraction lines with the operation and = sign ( --- ( ) --- = ---)

9. Write the new-circled denominator under each three fraction lines

10. Multiply the garage number to their numerators and write those numerators on your new fraction lines ------( ) ------= ------

11. Use the operation you circled (from number 3) to solve the numerators

12. All Done Do#Work#Here# !

APPENDIX B: BASELINE MULTIPLICATION TASK ANALYSIS

1. Write out all the fractions and what they are (you can just write first letter)

Fraction What Fraction What Fraction What

2. Cross out any fractions you don’t need.

3. Circle the operation to use: + − × ÷

4. Write out the fraction sentence with the operation you circled

5. Use the operation you circled to solve the numerators

6. Use the operation you circled to solve the denominators

7. Write out the numbers 1-12 below

8. Starting with 12, circle the number that has the numerator and the denominator in its number song.

9. Write the garage number the numerator is in from the circled number ! 10. Write a fraction line under that number

11. Write the garage number the denominator is in from the circled number

12. All Done Do#Work#Here# !

APPENDIX C: MULTIPLICATION TASK ANALYSIS WITH SPECIAL INTERESTS

Write out all the fractions and what they are under each fraction

Write out the Hurry up and Fraction Fraction Cross out the Circle the Operation Fraction fraction sentence multiply those fractions you to use with the operation don't need numerators cause you circled from I've got some Tweety!! What What What rabbit hunting to do y'all!

Comon now and Starting with 12, Write the new Now go ahead write those numbers circle the number fraction by writing EH! Whats Up Doc! Your Done! and multiply the 1-12 in your work that has the the garage number denominators Now tell me one of your favorite schedule area numerator and the the numerator and Looney Tune Episodes! denominator in its denominator is in multiplication song from the number you circled.

Do your Work Schedule Here

APPENDIX D: SUBTRACTION TASK ANALYSIS WITH SPECIAL INTERESTS

Write out all the fractions and what they are under each fraction

Common Denominators Fraction Fraction Write out the fraction Fraction Circle the Operation Circle One garages and circle the Cross out the fractions you to use least common Yes = solve numerators denominator don't need doc What What What No = do what Taz says to do

Multiply the garage Rewrite the new Use the operation Write the new Write the new number to their garage number fraction lines with numerators and you circled from beside both new the operation you circled denominator write those me to solve the under each three numerators. circled denominators circled with numerators on your Thats all Folks! Foghorn Leghorn fraction lines new fraction lines ( ) = ( ) =

Do your Work Schedule Here

APPENDIX E: STORY PROBLEM EXEMPLARS

Baseline and Maintenance Story Problems Joan planted 5/8 rows of spinach and 5/6 rows of beans in a garden. Joan also planted 2/6 a row of poplar trees. In total, how many rows of vegetables did Joan plant?

Sally was told to drink 9/10 cups of milk. Sally drank 5/6 of a cup of milk at lunch and 1/8 of a cup of milk at dinner. In total, how many cups of milk did Sally drink today?

Story Problems with Special Interests Bugs Bunny had to complete chores. Bugs Bunny has completed 3/7 of the house chores and 4/6 of the yard chores. Bugs Bunny had a dream he only had to do 1/8 of the chores. What fraction of all the chores has Bugs Bunny done?

Tweety Bird ate 2/8 of a pie on Monday and 2/7 of a pie on Tuesday. On Wednesday, Tweety Bird ate 3/6 of a pie. How much pie did Tweety Bird eat on Monday and Tuesday?

APPENDIX F: ADDITION AND SUBTRACTION DATA SHEET

Observer Initials: ______Date: ______

Condition: Baseline, Intervention, Main./Gen. Video Number: ______

Operation + - x Condition Day: ______

Task Analysis Steps IRT Correct/Incorrect Self Correct Notes

Write Fraction 0 1

Write Item 0 1

Write Fraction 0 1

Write Item 0 1

Write Fraction 0 1

Write Item 0 1

Erase or Cross Out 0 1 Fraction Circle the Operation 0 1

Circle Common 0 1 Denominator Write Fraction Garages 0 1 and Circle LCD (Response 1) F LCD (Response 2) F 0 1

LCD (Response 3) G 0 1

LCD (Response 4) G 0 1

LCD (Response 5) Circle 0 1

Write Garage Number 0 1

Write Fraction Sentence 0 1

Write New Denominator 0 1

Multiply Garage # 0 1

Solve Numerators 0 1

Totals.

F = Write Fraction; G = Write out Garages (Multiples); C = Circle the Common Denominator

APPENDIX G: MULTIPLICATION DATA SHEET

Observer Initials: ______Date: ______

Condition: Baseline, Intervention, Main./Gen. Video Number: ______

Operation: + - x Condition Day Number: ______

Task Analysis Steps IRT Correct/Incorrect Self Correct Notes

Write Fraction 0 1

Write Item 0 1

Write Fraction 0 1

Write Item 0 1

Write Fraction 0 1

Write Item 0 1

Erase or Cross Out 0 1 Fraction Circle the Operation 0 1

Write Fraction 0 1 Multiplication Sentence

Multiply the numerators 0 1

Multiply the 0 1 denominators Write out numbers 1-12 0 1

Circle Least multiple 0 1 from numbers 1-12 Write garage number the 0 1 numerator is in Write a fraction line 0 1

Write the garage number 0 1 the denominator is in

Totals

APPENDIX H: MSWO DATA SHEET

MSWO Data Sheet

Items Session 1 Session 2 Session 3 Session 4 Session 5 Total

Rank items from the lowest total to the highest total to obtain a preference hierarchy, where the lower number indicates a higher preference.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Notes:

APPENDIX I: PROCEDURAL FIDELITY CHECKLIST

Procedural*Fidelity*Checklist* ! Participant:!______* * * * Date:!______! ! Experimental0Condition:!!Addition,!Subtraction,!Multiplication! ! Experimental0Condition0Day0Number:!!______! Video*Number:!______! ! ! Experimenter’s*Behavior* Yes* No* Notes* (Procedure)* 1* Use!of!visual!schedule!depicting! ! ! ! work!requirements! 2* Modeled!the!entire!first!word! ! ! ! problem!(I*do)! 3* The!student!tried!the!second!word! ! ! ! problem!with!experimenter! providing!least!to!most!prompts!as! needed!(We*do)! 4* The!student!was!given!the!third! ! ! ! word!problem!as!a!test!(You*do)! 5* Least!most!prompting!sequence! ! ! ! occurred!after!20!seconds!of!IRT! 6* Positive!reinforcement!in!the!form! ! ! ! of!praise!and!preferred!activity! contingent!on!completing!visual! schedule! 7* During!the!test,!the!experimenter! ! ! ! sat!two!feet!away!with!attention!to!a! different!task.!! !