High-Tech or Low-Tech? Comparing Self-Monitoring Systems to Increase Task Independence for Students With Autism

Abstract

Independence is the ultimate goal for students with disabilities, including secondary students with autism. One avenue targeted for increasing independence and decreasing prompt-dependency is through self-monitoring. In this study, investigators sought to determine whether a difference exists in levels of task independence when three students with autism complete food preparation tasks while self-monitoring using a low-tech treatment (paper/pencil) and high-tech treatment (iPad). Although both interventions decreased the need for prompting thereby increasing independence, students needed less assistance when using the iPad. Students also maintained their levels of independence in food preparation following summer vacation. Social validity interviews indicated students preferred self-monitoring with the iPad over the paper/pencil.

Keywords

high school age functional skills independence autism spectrum disorders daily living skills intellectual disability

Independence in daily living is an important skill for all students to develop. However, students with autism struggle with independence and typically rely on others to assist them in everyday tasks (Hume, Loftin, & Lantz, 2009; Smith, Maenner, & Seltzer, 2012). Adult assistance (i.e., prompting) can help students with task completion initially. Without proper fading of prompting over time and implementation of interventions that successfully increase independence, dependency can occur and limit current and future opportunities for students (e.g., career opportunities; Hume et al., 2009). One method to increase independence for students with disabilities is to teach self-determination skills (Wehmeyer & Palmer, 2003). Wehmeyer (1996) defined self-determination as “acting as the primary causal agent in one’s life and making choices and decisions regarding one’s quality of life free from undue external influence or interference” (p. 18). A major component of self-determination is the ability to self-monitor one’s behavior (Wehmeyer, Gragoudas, & Shogren, 2006).

Self-monitoring occurs when a student assesses his or her own behavior to determine whether the desired behavior occurred and then records the occurrence or non-occurrence of the behavior (Hume et al., 2009; Lee, Simpson, & Shogren, 2007; Lienemann & Reid, 2006). It can be an effective way to facilitate behavior change or improve student behavior. The use of self-monitoring offers several benefits for students including (a) increase in self-reliance, (b) decrease in overreliance on external sources of monitoring (e.g., teacher, paraprofessional), (c) increase in instructional time, and (d) improvement in overall quality of life (Hume et al., 2009; Lee et al., 2007; Wehmeyer, Hughes, Agran, Garner, & Yeager, 2003).

In previous literature, self-monitoring improved student behavior and task-completion skills for students with emotional or behavioral disabilities (Bruhn & Watt, 2012; Gulchak, 2008), students with learning disabilities (Crabtree, Alber-Morgan, & Konrad, 2010; Harris, Friedlander, Saddler, Frizzelle, & Graham, 2005), and students with intellectual disability (Agran et al., 2005; Hughes et al., 2002). Self-monitoring also resulted in greater task independence and completion for students with autism (Ganz & Sigafoos, 2005; Holifield, Goodman, Hazelkorn, & Heflin, 2010; Parker & Kamps, 2011). Ganz and Sigafoos used tokens and blocks to help students with autism self-monitor during vocational tasks. Holifield et al. used pictures, verbal cues, and circling yes or no to help student’s self-monitor on-task behavior during independent academic activities in their self-contained classroom. Parker and Kamps used self-monitoring to promote functional skills and verbal interactions using task analysis. The students in this study checked a box after each step was completed in the task analysis across multiple in school settings.

More recently, technologies were used to support students with autism in self-monitoring. Cihak, Wright, and Ayres (2010) used technology for students with autism to learn self-monitoring skills. The researchers used static pictures displayed on a handheld computer illustrating expected behavior while students self-monitored with “yes” and “no” index cards to stay on task and demonstrate appropriate behavior. Students were able to successfully self-monitor their behavior in multiple settings. Legge, DeBar, and Alber-Morgan (2010) used the MotivAider®, a vibrating pager-like device, and a self-recording sheet to keep students with autism on task during independent mathematics work. The use of the MotivAider® lead to a quick and significant increase in the students’ on-task behavior. While these studies used technology to cue students to self-monitor, the students used paper and pencil to record their behavior (i.e., tallying or checking off tasks on a piece of paper).

Prior research suggests both low- and high-tech self-monitoring is effective for students with autism, but a lack of research exists using technology as a recording device for this population. Because students with autism typically have difficulty with fine motor functioning (Dawson & Watling, 2000) and may exhibit difficulty in using a pencil, the use of technology may accommodate recording of self-monitoring. The purpose of this study was to compare traditional paper/pencil self-monitoring to technology-based self-monitoring to determine whether using technology to cue, as well as record self-monitoring, produces an added benefit over using traditional methods. The specific research questions for the study are as follows:

Research Question 1: Will students increase levels of task independence when self-monitoring?

Research Question 2: Will the number of prompts and task independence differ when comparing traditional paper/pencil and technology-based tools?

Research Question 3: Will the time it takes students to complete tasks differ when comparing traditional paper/pencil and technology-based tools?

Research Question 4: What are students’ perspectives of using paper/pencil or technology to self-monitor?

Method

Participants

Three secondary students attending public school in a self-contained setting in the Midwest served as participants in this study. Katie, Chris, and Mia, as well as a fourth student, were nominated by their teacher for participation in this study based on the following criteria: (a) identified as having a primary disability of autism spectrum disorder, (b) sufficient fine motor ability to operate the technology, (c) adequate health to participate in the study (i.e., reliable attendance records), and (d) demonstrated dependence on external prompting for transitioning and performance through tasks. Successful completion of a pretraining program using both paper and pencil and the iPad was also required for students to move into the intervention phases of the study. A fourth student was originally selected for participation, but excluded for failing to pass the pretraining program. All students received daily instruction across the domains of daily living including domestic, community, vocational, and recreation/leisure skills with additional focus on functional academics, and social and communication skills. Table 1 provides a summary for each student.

Table 1.

Student Characteristics.

Student	Age	Ethnicity	Primary disability	Secondary disability	IQ^a	Adapted behavior scale	Autism rating scale
Katie	13	Caucasian	Autism	Language impairment	NR	50^b	Average probability of autism ^c
Chris	15	Caucasian	Autism	Language impairment	NR	58^d	Severely autistic range ^e
Mia	15	Caucasian	Autism	Language impairment	57^a	63^f (teacher)	Mildly moderately autistic range ^e
						49^f (parent)

Note. NR = not reported.

Wechsler Preschool and Primary Scale of Intelligence–Revised (WPPSI-III). ^bAdapted Behavior Assessment System II (ABAS-II) Composite. ^cGilliam Autism Rating Scale (GARS) teacher rating. ^dVineland Adapted Behavior Scale (VABS) Composite. ^eThe Childhood Autism Rating Scale (CARS). ^fVineland Adapted Behavior Scales–Interviews.

Katie was a 13-year old female identified with autism and, based on teacher report in the Individualized Education Program (IEP), functioning at a moderate level of intellectual ability. Katie also received services for Language Impairment. Annual IEP goals indicated a focus on functional academics (e.g., exchanging correct amounts of money), domestic skills (e.g., following multiple steps in a recipe), self-care skills (e.g., completing hygiene routines independently), and communication skills (e.g., increasing appropriate conversational skills). Katie worked well with others, smiled often, and demonstrated compliance with tasks when a teacher or paraprofessional provided external verbal prompts for each step. According to her teacher, Katie was highly dependent on verbal prompts for task completion across all domains of daily living and experienced difficulty with gross and fine motor tasks. Throughout the study, Katie independently communicated with unfamiliar individuals (e.g., verbalizing aversion to eating certain recipes she prepared) and complied with completing all food preparation tasks.

Chris was a 15-year-old male identified with autism and, based on teacher report in the IEP, functioned at the severe level of intellectual disability. Chris was nonverbal and received services for Language Impairment. He was able to respond accurately to yes/no questions when responses were displayed visually (i.e., yes/no cards). Throughout the study, Chris responded to questions using pictures or one-to-two word verbalizations (e.g., yes, no, more please). Annual IEP goals indicated a focus on vocational skills (e.g., entering codes into the computer, wiping tables), functional academics (e.g., exchanging correct amount of money), domestic skills (e.g., following multiple steps in a recipe), and communication skills (e.g., using his augmentative and alterantive communication [AAC] device to increase communication exchanges). According to his teacher, Chris was motivated to work with technology; playing on the computer and iPad were two of his favorite activities in school. Chris was sensitive to loud or soft external noise. For example, when the microwave was on during food preparation tasks he paused his current task to plug his ears and engage in vocal stereotypy (e.g., humming). Once the microwave turned off, he would resume his task.

Mia was a 15-year-old female identified with autism and functioned at the mild level of intellectual disability, according to IQ data, and received services for Language Impairment. Annual IEP goals indicated a focus on social skills (e.g., maintaining personal space), vocational skills (e.g., following a task list), and communication skills (e.g., keeping private information to herself). Mia was a sociable young woman who often voiced personal opinions and frequently required verbal reminders to use an inside voice and keep her hands to herself. She demonstrated more motivation preparing unhealthy food choices (e.g., ice cream sundae) in comparison with healthy options (e.g., fruit smoothie). According to her teacher, Mia asserted independence and did not always comply with completing tasks in a predetermined order.

Setting

The research took place at a middle/senior high school in the Midwest located in a rural setting. All sessions were conducted in the students’ special education class that served the participants for a majority of the school day. The classroom was divided into life skills and academic sides. The academic side of the classroom was carpeted and filled with desks. The life skills side was separated by a removable wall partition and included tile flooring, round tables, lockers, a washer and dryer, living area (i.e., three comfort chairs, a bulletin board, and boom box), access to a restroom, and a full-service kitchen. All sessions were conducted in the life skills side of the classroom when other students were working on the academic side. Sessions took place in the kitchen area furnished with two microwaves, a stove, refrigerator, sink, and small appliances on a counter along one wall (e.g., toaster, blender). The kitchen cabinets were stocked with food and cooking supplies.

Materials

Apple iPad 2

Two Apple second-generation iPads were used to take pictures of the ingredients and deliver technology-based recipes with self-monitoring checkboxes to students. These devices were 9.5 × 7.3 × 0.34 inches in size and contained a 16GB storage capacity. Each device was contained in an adapted foam case thick enough to prevent damage if a spill were to occur near the iPad.

Upad lite app

To create the iPad recipes used during the intervention and maintenance phases of the study, the Upad lite app was downloaded onto the iPads. The Upad lite app was a note-taking app that allowed researchers to import photos from the iPads’ photo albums to create pictorial recipes. The app also allowed text to be inserted, images to be imported from the web (e.g., Google), and offered multiple template options (e.g., yellow lined, plain white, graphing paper). The lite version was free, but only allowed five notes (i.e., recipes) to be created and stored at a time. The full version of this app cost US$4.99.

Recipes

Recipes across all phases of the study consisted of a 10-step task analysis (i.e., 10 recipe steps) with four ingredients in each recipe. All participants were assigned the same recipes to complete. Each recipe was completed only once by each participant either in the paper/pencil or iPad condition—depending on the randomization predetermined for each student. Researchers controlled recipes for treatment comparison by ensuring an identical number of steps and ingredients for each recipe across both treatments. Each recipe presented a title, photos of the four required ingredients, and steps numbered 1 to 10 with each step including text and pictorial instructions. The number of pictures for each step varied dependent on the complexity of the step. For example, pouring water into the blender included two pictures: (a) picking up the water and (b) pouring the water into the blender. Pouring ketchup onto a plate included three pictures: (a) opening the ketchup, (b) pouring it, and (c) closing the top. For paper/pencil recipes, three to four recipe steps fit on each page, and, for iPad recipes, only two steps fit on each page. Figure 1 provides a sample of a receipt for the paper/pencil condition; recipes for the iPad were similar in structure.

Figure 1.

Soup and sandwich paper/pencil recipe.

Dependent and Independent Variables

Three dependent variables were targeted for intervention: (a) the number of recipe steps students completed independently per recipe, (b) the number of prompts students required to complete each recipe, and (c) and the total duration of each recipe. The dependent variables were important in demonstrating levels of student independence during intervention.

The independent variable was a package—the system of least-to-most prompts in conjunction with self-monitoring. For the self-monitoring, students engaged in two treatments: paper/pencil-based recipes with a self-monitoring checklist and technology-based recipes with a self-monitoring checklist. The self-monitoring consisted of checkboxes in which students checked via pencil on the paper/pencil recipes or via their finger on the iPad technology-based recipes. A simple checklist was used because this a simple means of self-monitoring—both for teaching students as well as for teachers to implement. The system of prompts occurred in the following order: (a) non-specific verbal, (b) specific verbal, (c) verbal and gestural, (d) model, (e) partial physical assistance, and (f) hand-over-hand physical assistance.

Experimental Design

An alternating treatment design (ATD) was used to illustrate the effectiveness of two self-monitoring treatments on increasing independence with food preparation tasks (Wolery, Gast, & Hammond, 2010). This design was selected because it allowed researchers to compare a paper/pencil self-monitoring intervention with a technology-based self-monitoring intervention. In addition, the design enabled a quick determination of the effectiveness of the interventions in food preparation tasks and identified the most effective treatment for each participant (Wolery et al., 2010). The design included a baseline phase, comparison phase, best treatment phase, and maintenance phase. During the comparison phase, interventions were selected randomly in a counterbalanced fashion for each participant. Specifically, 10 pieces of paper (5 representing the paper/pencil treatment and 5 representing the iPad treatment) were placed in a hat and drawn at random. This process was carried out for each participant separately. No more than two sessions of the same treatment occurred consecutively throughout the comparison phase. Following the comparison phase, the best treatment phase included the superior treatment for each participant. The best treatment was identified by dividing the number of steps completed independently by the total number of task steps for each treatment. The maintenance phase occurred 14 weeks after completion of the last sessions in the best treatment phase and included two sessions using the superior treatment for each participant.

Data Collection

Two data collection methods were used during this investigation. Event recording was used to (a) document the number of task steps students completed independently for each recipe, (b) record the number and type of prompts each student required to complete each task when unable to do so independently, and (c) only for intervention data, record the number of steps that was independently self-monitored (i.e., check placed in checklist) for each recipe. Duration recording was used to record the time in minutes and seconds students required to complete a recipe. Both event recording and duration recording procedures were selected as they provided a complete representation of students’ actions during baseline and intervention sessions.

Experimental Procedures

Baseline

Baseline consisted of six to eight sessions where students completed food preparation tasks using picture steps. Baseline was conducted to determine students’ level of independence with completing a recipe without a self-monitoring component (i.e., no checklist). Students began each session by washing their hands and sitting at a round table containing all materials required for the session (i.e., materials, ingredients, and a 10-step picture recipe)—except the microwave, toaster, and blender, which were located on cabinets by the sink in the kitchen area. Researchers then read a script and shared the following information with students via verbal and visual cues: (a) what students were going to make; (b) ingredients students would use; (c) directions and pictures for the recipe. The researcher pointed to a pictorial recipe and stated, “Today we will be making [recipe name]. These pictures show you what to do to make the food.” Students were then prompted to begin.

Researchers recorded the time students started the food preparation task, the level of assistance required for completion of each step of the recipe (i.e., independent or level of prompting required), and the end time when students completed the food preparation task. After 10 s of off-task or no activity from the student, the researcher began the previously designed scripted prompting sequence (i.e., the non-specific verbal prompt “What’s next?”). If a student deviated from the directions (e.g., began Step 4 before completing Step 3), the researcher gave a scripted non-specific verbal prompt (e.g., “Stop, number three is next”). The prompting sequence was continued until the student completed the step.

Pretraining

The pretraining phase consisted of two parts and occurred after baseline. For the first part of the pretraining phase, students reviewed basic competencies with a researcher using the technology until they could demonstrate (a) turning on the iPad, (b) swiping screens, (c) making a check mark or mark representative of a check in the checkbox, and (d) how to turn pages in the Upad lite app to view all recipe steps. The second part of pretraining taught students to use a checklist for both treatment conditions; each step of each recipe had a checklist. Specifically, students completed non-food preparation tasks that were represented via a checklist with instructions consisting of text and photos. As students completed each step, they were taught to place a check in a checkbox with a pencil on the paper recipe for the paper/pencil condition and with their finger on the iPad for the technology condition (see Figure 1). Criteria for passing pretraining required students to complete eight steps consecutively independently for both treatment conditions, starting with the paper/pencil condition. Tasks included a series of 10 one-step instructions in the life skill areas of (a) money (e.g., place a stack of dollar bills in the box), (b) recreation (e.g., put puzzle pieces into the bucket), and (c) domestic tasks (e.g., put the clothes into the washer). Next, students completed a series of non-food tasks similar to those with pencil/paper, but with the iPad.

Intervention

After meeting pretraining criteria, students entered the intervention phase. During intervention, two treatments were compared: paper/pencil and the iPad. Across 12 to 14 sessions, students completed an equal number of food preparation tasks under each treatment (i.e., 6 recipes for each treatment or 7 recipes for each treatment). Two students completed 12 sessions, but one student required an additional session for each treatment to determine the best treatment. The intervention recipes appeared similar to baseline recipes, with the exception of the added checkboxes for self-monitoring. Checkboxes were located on the right side of the paper or iPad screen, on the same line as each recipe step.

Procedures during intervention were consistent with the baseline phase with two exceptions: (a) the self-monitoring component was added (i.e., students completed the checklist) and (b) when researchers read the instructions script, students were reminded to turn the pages to continue the recipe until all 10 steps were done. During baseline, researchers noticed students were not turning pages independently to continue the recipe. A reminder to turn paper/pencil and iPad pages during intervention sessions was added to the intervention script read by researchers before each session started.

Researchers—the second, third, and fifth authors who were current special education doctoral students and former special education teachers—used scripts during intervention, similar to the baseline scripts, to enhance treatment integrity. Researchers recorded (a) the level of assistance required for completion of each step of the recipe; (b) whether the student self-monitored for each step of the recipe (i.e., placed a mark in checkbox); and (c) the total time students needed to complete the food preparation task. For the prompting sequence, after 10 to 15 s of off-task or no activity from the student, the researcher provided a prompt for the next step. During baseline and pretraining sessions, researchers noticed Katie and Chris began performing a step just as they were about to be prompted. Researchers decided to give them a 15-s wait time before the prompting sequence began; wait time remained 10 s for Mia.

Best treatment

The best treatment phase occurred for five sessions in which students prepared food under the self-monitoring condition she or he was most successful (i.e., pencil/paper or iPad). The condition in which students were most successful was the condition in which students accurately completed the most task analysis steps independently; this was determined via percent nonoverlapping data (PND; Gast & Spriggs, 2010). Procedures remained consistent from the comparison phase.

Maintenance

The maintenance phase was conducted 14 weeks after the last best treatment phase session. Students prepared food using the superior treatment for two sessions. Procedures remained consistent from the comparison and best treatment phases.

Data Analysis

Data analysis was conducted by inspecting between- and within-phase patterns for dimensions of (a) level, (b) trend, (c) variability, and (d) overlapping data (Kennedy, 2005). Data variability and stability were examined within and across phases with stability defined as at least 80% of the data falling on or within a 20% range of the median level for all data points in each condition (Gast & Spriggs, 2010). Trend was determined using the split-middle method (Gast & Spriggs, 2010; White & Haring, 1980). For an ATD, PND within the comparison phase is the most critical statistic to report (Wolery et al., 2010). PND within the ATD compares each treatment session against each other (i.e., first iPad session to the first paper/pencil session, second to second, and so forth) and PND is calculated by taking the number of sessions a treatment is superior to the other treatment and dividing by the total number of sessions (Wolery et al., 2010). For example, if the iPad treatment was superior to the paper/pencil treatment on five out of six sessions, PND would be (5/6) × 100 = 83%. The PND between treatments was used to determine the best treatment for each participant.

Inter-Observer Agreement and Treatment Fidelity

Two researchers recorded inter-observer agreement data (IOA) simultaneously but independently from one another during 43% of baseline sessions, 32% of comparison sessions, and 40% of best treatment sessions. Inter-observer agreement for the three dependent measures was calculated by dividing the number of recorded agreements by the number of agreements plus disagreements and then multiplying by 100. For duration recording, total time was considered an agreement if within 5 s of one another. The inter-observer agreement for the number of recipe steps completed independently ranged from 98% to 100% for each student (µ = 98.5%). Inter-observer agreement was 98% for Katie, 100% for Chris, and 98% for Mia. Inter-observer agreement for the total duration of each session measured ranged from 78% to 89% (µ = 84%). Inter-observer agreement for duration was 89% for Katie, 86% for Chris, and 78% for Mia. Inter-observer agreement for duration was low due to researchers using different methods to time the sessions in baseline (i.e., stop watch vs. an analog clock). The inter-observer agreement for the number of prompts students required to complete each recipe measured was 90% to 97% (µ = 93%). Inter-observer agreement was 92% for Katie, 97% for Chris, and 90% for Mia. For prompting, time values collected by researchers were considered in agreement if they were within 2 s of one another.

Treatment fidelity measures confirmed the correct and consistent use of intervention procedures as researchers facilitated student completion of food preparation tasks during 32% of comparison phase and 40% of best treatment phase intervention sessions. An independent observer used an itemized data sheet with a task analysis of steps the researcher was to carry out with the participant. Agreement was measured by dividing the number of correctly implemented steps observed by the total number of possible steps and then multiplying by 100. Overall mean agreement was 99% for all participants. Independent observers indicated on the data sheet if the researcher (a) read the script, (b) pointed to recipe steps, (c) started timer to record duration, (d) recorded self-monitoring data, (e) recorded independent or level of prompt for each step of recipe, (f) waited 10 or 15 s to start prompt sequence, depending on particular student, (g) followed least-to-most prompting sequence, and (h) recorded end time for duration recording.

Social Validity

Social validity measures were collected twice for both students and the students’ teacher of record following baseline and after completion of the best treatment phase. The teacher completed a questionnaire related to students’ food preparation, self-monitoring strategies, and technology usage. In addition to the teacher questionnaire, the students answered questions related to experiences making food, preferences on using a paper/pencil checklist versus the iPad, and what they believed they needed to help them make food by themselves. Students responded verbally or pointed to pictorial responses.

Results

Student performance data across participants and phases are provided in Table 2. Figure 2 depicts the percentage of task independence for each session across all phases of the study for Katie, Chris, and Mia. All students demonstrated an increase in independent task performance between baseline and intervention phases while decreasing the number of prompts needed to complete food preparation tasks for both treatment conditions. However, the use of self-monitoring did not decrease the time it took for students to complete the recipe for both treatments. With paper/pencil self-monitoring, duration increased for all students and with iPad self-monitoring, duration decreased for two of the three students. Student perspectives were positive for both treatment conditions, with preference given to the iPad.

Table 2.

Student Performance Summary Across Each Phase.

Phase	Participants	Task independence M (%)	Prompts required	Self-monitoring M (%)	Duration
Baseline	Katie	10	20	NA	9 min 36 s
	Chris	23	15	NA	8 min 06 s
	Mia	43	13	NA	6 min 42 s
PP comparison	Katie	63	7	73	14 min 06 s
	Chris	72	5	50	9 min 18 s
	Mia	71	4	86	8 min 00s
iPad comparison	Katie	73	5	95	11 min 00 s
	Chris	82	3	90	6 min 54 s
	Mia	73	3	89	5 min 54 s
Best treatment^a	Katie	90	1	100	9 min 54 s
	Chris	84	2	92	6 min 06 s
	Mia	90	1	92	5 min 36 s
Maintenance^a	Katie	80	3	95	8 min 48 s
	Chris	70	5	20	7 min 07 s
	Mia	90	1	90	5 min 55 s

Note. PP = paper/pencil.

iPad.

Figure 2.

Percentage of independent steps for food preparation tasks across phases.

Katie

Baseline

Katie completed six baseline sessions to demonstrate stability and a zero celerating trend. More than 80% of Katie’s baseline data points fell within a 20% range of the median indicating stability (Gast & Spriggs, 2010). Baseline data produced a range of 0% to 30%, averaging 10% of the steps completed independently per session. Recipes averaged 9 min 36 s to prepare and Katie required an average of 20 prompts per session.

Intervention

During the comparison phase, Katie completed a total of 12 recipes with 6 recipes in each treatment. For both conditions, more than 80% of Katie’s intervention data points fell within a 20% range of the median indicating stability (Gast & Spriggs, 2010). For both treatments, Katie’s trend was decelerating. The iPad treatment was superior to the paper/pencil treatment for three of six sessions, yielding a PND of 50%. The paper/pencil treatment was superior to the iPad treatment for two of six sessions, yielding a PND of 33.3%, indicating the iPad the superior treatment for Katie. Katie completed 63.3% steps independently during paper/pencil sessions and 73.3% during iPad sessions. Katie placed marks in checkboxes as an indication of self-monitoring for 73% of paper/pencil sessions and 95% of iPad sessions. Total duration averaged 14 min 6 s for paper/pencil sessions and 11 min for iPad sessions. Katie required an average of seven prompts per session in paper/pencil sessions and five prompts during iPad sessions.

Best treatment

During the comparison phase, Katie was most successful preparing food while self-monitoring with the iPad. During the best treatment phase, Katie completed five recipes using an iPad to self-monitor, checking off 100% of the steps for each completed recipe. Task independence data produced a range of 70% to 100%, with an average of 90% of the steps completed independently per recipe. The average duration for each session during this phase was 9 min 54 s and Katie required an average of one prompt per session.

Maintenance

Katie performed similarly in her two maintenance sessions, completing 80% of steps independently while self-monitoring an average of 95% of tasks. The average duration for each session was 8 min 48 s and the average of three prompts was per session.

Chris

Baseline

Chris completed seven baseline sessions to demonstrate a stability and decelerating trend. More than 80% of baseline data fell within a 20% range of the median indicating stability (Gast & Spriggs, 2010). Chris’s data for baseline sessions produced a range of 0% to 70%, with an average 23% of the steps completed independently. Recipes averaged 8 min 6 s to prepare and required an average of 15 prompts per session.

Intervention

During the comparison phase, Chris completed six recipes in each condition. For both conditions, more than 80% of Chris’s intervention data points fell within a 20% range of the median indicating stability (Gast & Spriggs, 2010). For paper/pencil, Chris’s trend was zero celerating, but he had an accelerating trend for the technology condition. The iPad treatment was superior to the paper/pencil treatment for 4 out of six sessions, yielding a PND of 67%. Chris completed 72% steps independently during paper/pencil sessions and 82% during iPad sessions. Chris placed marks in checkboxes as an indication of self-monitoring for 50% of paper/pencil sessions and 90% of iPad sessions. Total duration averaged 9 min 18 s for paper/pencil sessions and 6 min 54 s during iPad sessions. Chris required an average of five prompts per session during paper/pencil sessions and three prompts during iPad sessions.

Best treatment

During the comparison phase, Chris was most successful using the iPad. During the best treatment phase, Chris completed five recipes using an iPad to self-monitor, checking off 92% of the steps for each recipe. Task independence data presented a range from 70% to 100%, with an average of 84% of the steps completed independently per recipe. The average duration for each session was 6 min 6 s, and Chris required an average of two prompts per session

Maintenance

During maintenance, Chris completed 60% of steps independently in Session 1 and 80% of steps independently in Session 2 (µ = 70%). Chris self-monitored an average of 20% of tasks. The average duration for each session was 7 min 7 s and Chris required an average of five prompts per session during this phase.

Mia

Baseline

During baseline, Mia completed eight sessions to demonstrate stability and a decelerating trend. More than 80% of baseline data fell within a 20% range of the median indicating stability (Gast & Spriggs, 2010). Baseline data produced a range of 20% to 70%, with an average of 43% of the steps completed independently per session. Recipes averaged 6 min 42 s to prepare and Mia required an average of 13 prompts per session.

Intervention

After completing six intervention sessions for each treatment, data indicated Mia performed equally with both. Thus, Mia completed one more recipe in each treatment for a total of 14 recipes completed during intervention. In both conditions, more than 80% of Mia’s data points fell within a 20% range of the median, indicating stability (Gast & Spriggs, 2010). In terms of trend, Mia was accelerating for the iPad treatment and zero celerating for the paper/pencil treatment. The iPad treatment was superior to the paper/pencil treatment for four out of seven sessions, yielding a PND of 57%. Mia completed 71% steps independently during paper/pencil sessions and 73% during iPad sessions. Mia placed marks in checkboxes as an indication of self-monitoring for 86% of paper/pencil sessions and 89% of iPad sessions. Total duration averaged 8 min during paper/pencil sessions and 5 min 42 s during iPad sessions. Mia required an average of four prompts per session in paper/pencil sessions and three prompts in iPad sessions.

Best treatment

During the best treatment phase, Mia completed five recipes using the iPad, checking off 92% of the steps as an indication of self-monitoring. Task independence data showed a range between 80% and 100%, with an average of 90% of the steps completed independently per recipe. The average duration for each session was 5 min 36 s and Mia required an average of one prompt per session during this phase.

Maintenance

During maintenance, Mia completed 100% of steps independently in Session 1 and 80% of steps independently in Session 2 (µ = 90%). The average duration for each session was 5 min 55 s and she required an average of one prompt per session during.

Social Validity

In an interview following baseline, the teacher predicted the students might prefer the technology and remain more motivated with the iPads compared with the paper/pencil method. In an interview after the best treatment phase, the teacher noticed the students worked more independently when using a checklist and indicated she would use iPads during food preparation tasks if the technology was available. In early interviews, Katie thought she would like the paper/pencil method, whereas Chris and Mia thought they would prefer using the iPad. After the best treatment phase, all three students expressed preference for using iPads over paper/pencil recipes to make food. Students reported iPads helped them to complete recipes independently and they enjoyed looking at photos and making self-monitoring marks.

Discussion

With new and sophisticated technologies becoming commonplace in classrooms (Kim & Hannafin, 2011), it is important to determine the advantages and disadvantages these technologies afford to students as compared with no-tech or low-tech options. The purpose of this project was to determine whether paper/pencil or use of an iPad and an app would be more effective and efficient for teaching self-monitoring skills within the context of food preparation tasks for secondary students with autism. The major findings of this study were (a) self-monitoring with both paper/pencil and iPad interventions increased task independence and reduced the number of prompts students needed per session, including maintaining such results for 14 weeks; and (b) the iPad was the more effective, efficient, and preferred system for self-monitoring.

Our findings add to previous literature demonstrating self-monitoring to be an effective intervention for promoting independence for students with autism (Cihak et al., 2010; Ganz & Sigafoos, 2005; Holifield et al., 2010; Legge et al., 2010; Parker & Kamps, 2011). Prior to the intervention, all three students were dependent on externally delivered prompts to complete recipe tasks with no student able to complete a recipe independently. Following intervention, each student was able to complete at least two recipes independently. Not only did the students gain independence by learning to self-monitor their behavior but also the students expressed positive feelings about their ability to independently prepare food items.

While previous studies using self-monitoring with students with autism only included technology as the cueing device to self-monitor and used more traditional methods to record (Cihak et al., 2010; Legge et al., 2010), this study used technology as both the cueing and recording device—thus expanding the literature by comparing high- and low-tech self-monitoring interventions for students with autism. While students required fewer prompts and demonstrated increased independence using both interventions, students performed optimally with the iPad. All three students were more independent when using the iPad app and completed food preparation tasks in less time than when using paper/pencil. The greater effectiveness and efficiency likely contributed to their preference for the iPad. As a result, students with autism might maintain higher levels of engagement using high-tech devices compared with low-tech options. In addition, using a pencil appeared to create some barriers for the students, which may reflect students’ lower fine motor functioning (Dawson & Watling, 2000). Occasionally, students would drop the pencil or had to look for it because it was lost among the food items on the table. With Katie’s lack of dexterity, she struggled to hold it correctly before making a mark. Also, students typically made a complete check when using a pencil, but produced partial marks when using their finger during iPad recipes, resulting in taking less time to self-monitor on the iPad.

Implications for Practice

This study showed self-monitoring to be effective for both low- and high-tech treatments to increase independence during food preparation tasks. When determining whether to use low- or high-tech self-monitoring systems with students, practitioners should consider the practicality to the student as well as the time and cost to attain technology and perform the training (Snell & Brown, 2006). With use of a camera, computer, and printer, practitioners or paraeducators could create the low-tech self-monitoring system used in this study. When using the high-tech self-monitoring system, practitioners and paraeducators could create the recipes with checkboxes used in this study solely with the iPad. Practitioners and paraeducators may need to read through the Upad lite app user guide to become familiar with how to upload pictures and save recipes, but otherwise the iPad app self-monitoring intervention represents an easy and low-cost—if you have an iPad—intervention.

Because the study found increased independence when using technology to self-monitor, practitioners may want to integrate technology such as the iPad into a life skills curriculum as a tool to teach students independence in completing other daily living skills (e.g., cleaning the kitchen, making a bed, accessing transportation). Because high-tech devices may increase independence and engagement more so than low-tech devices due to ease of use and clearer visual display, teachers could use high-tech devices to increase engagement and motivation during tasks students find less interesting. However, student familiarity with both high- and low-tech tools are warranted, as students may not always have access to high-tech technology post-school, yet can—and should—continue to self-monitor if such practice results in increased independence (Bouck, Satsangi, Barlett, & Weng, 2012).

Practitioners may also want to consider the high level of maintenance exhibited by students in this study. It was notable that students were able to remember how to use the technology to self-monitor without receiving self-monitoring instruction during a 14-week break. This shows the promising potential for using self-monitoring to increase independence post-school. For students with autism, increasing independence while following directions may positively affect future learning opportunities with less time spent on re-teaching skills. Integrating student-preferred self-monitoring methods into the curriculum for students with autism can increase their potential to develop greater independence across settings in the school and community.

Limitations and Future Research

The results of this study provide support for considering the use of high- or low-tech methods to self-monitor performance; however, results must be interpreted within the context of its limitations. For example, all students who participated in the study were already familiar with the iPad having used it previously in the classroom to access games as positive reinforcement. It is possible that students held a predetermined preference for the iPad over paper/pencil, because they associated it with enjoyable activities. Students unfamiliar with an iPad may require more intensive training with the technology before using it for self-monitoring activities.

The collection of recipes may also have provided limitations. While the number of food items and steps contained within each recipe remained consistent across students, recipes varied in complexity and completion time due to students’ skill level and equipment required for recipes. Future investigations comparing treatments of self-monitoring should require tasks of similar difficulty and require similar time required for completion.

Another limitation was presented by the difference in visual displays of the iPad and paper/pencil. The iPad had only two steps displayed per page and may have illustrated information more clearly, whereas the paper/pencil recipes averaged three to four steps per page. Future investigations might determine how the number of task steps presented per page under each condition affects student performance. To enhance treatment fidelity, future studies comparing two treatments would require researchers to present mediums controlled for outside variables (e.g., visual display and format of task analysis).

Finally, there was not a clear and strong distinction between the two intervention treatments for the participating students. Relatedly, researcher bias may limit interpretation of findings, such as deference to the iPad condition. Observers were not naïve to the study (Gast, 2010). Due to lack of available independent, outside observers, researchers collecting data also served as independent observers for IOA and treatment fidelity procedures. However, data were collected independently and inter-observer communication was constrained through the study. In addition, one investigator was a former teacher in the classroom and this may have motivated students to demonstrate support for this researcher.

Footnotes

Authors’ Note

The second and third authors provided equal effort to this study, and thus their order should not be interpreted as a greater contribution over the preceding author.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by a grant from the Office of Special Education Programs to the first and fourth authors (H325D090003).

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