Research in Autism Spectrum Disorders 8 (2014) 1405–1415

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Research in Autism Spectrum Disorders

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Using video prompting via iPads to teach price comparison§

to adolescents with autism

Pei-Lin Weng a,*, Emily C. Bouck b

a Department of Special Education and Counseling, William Paterson University, Wayne, NJ 07470, USAb Department of Counseling, Educational Psychology, and Special Education, Michigan State University, East Lansing, MI 48824, USA

A R T I C L E I N F O

Article history:

Received 24 June 2014

Accepted 26 June 2014

Keywords:

Price comparison

Functional mathematics

Video prompting

Tablet computer

iPad1

A B S T R A C T

Price comparison is a functional mathematics skill commonly taught to secondary

students with autism and intellectual disability to increase independence; yet, a lack of

evidence-based practice in teaching price comparison exists. The purpose of this study was

to examine the effectiveness of video prompting to teach price comparison using an

adapted number line. A single-subject, multiprobe, multiple baseline design study was

employed across three secondary students with autism. The results showed two out of

three students benefited from video prompting presented on an iPad to complete price

comparison tasks during the in-class simulation and the grocery store settings. Of the

three students, one student completed price comparison tasks solely from video

prompting and the other two students required video prompting in conjunction with

the system of most-to-least prompts.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

For secondary students with autism and intellectual disability, a common goal for postschool life is maximizing theirindependence (Cihak & Grim, 2008). One way to help these students maximize their independence after leaving school is toprovide them with functional life skills during secondary school (Ayres, Mechling, & Sansosti, 2013; Hume, Loftin, & Lantz,2009). Students with autism and intellectual disability are often provided a functional or life skills curriculum in secondaryschool with the hope these students can apply the skills in everyday life (Alwell & Cobb, 2009). Among daily living skills,purchasing skills are the most widely researched (Alwell & Cobb, 2009; Xin, Grasso, Dipipi-Hoy, & Jitendra, 2005). However,within purchasing skills, price comparison receives the least attention in research (Browder, Spooner, Ahlgrim-Delzell,Harris, & Wakeman, 2008; Xin et al., 2005).

Price comparison ultimately involves consumers determining lower-priced products or products with better prices(Browder, Spooner, & Trela, 2011; Sandknop, Schuster, Wolery, & Cross, 1992). Price comparison is actually a multitude oftasks, starting with an understanding of number identification and numerical comparison (Browder et al., 2011; Storey &Miner, 2011). Numerical comparison is commonly taught through the use of a number line (Mosley, 2001). Despite the use ofnumber lines primarily through only the eighth grade in general education, the tool can assist secondary students withdisabilities with functional mathematical content, such as planned purchases, the next-dollar strategy, and price comparison(Browder et al., 2011). For example, Sandknop et al. (1992) employed an adapted vertical number line to teach price

§ This project was supported by a Purdue University Service Learning Grant to the first author, Purdue University, West Lafayette, IN 47097, USA.* Corresponding author at: 1101 S. State St., Apt 1107, Chicago, IL 60605, USA.

E-mail address: itat.sped@gmail.com (P.-L. Weng).

http://dx.doi.org/10.1016/j.rasd.2014.06.014

1750-9467/� 2014 Elsevier Ltd. All rights reserved.

P.-L. Weng, E.C. Bouck / Research in Autism Spectrum Disorders 8 (2014) 1405–14151406

comparison for students with intellectual disability. Although Sandknop et al. (1992) found a positive effect, vertical numberlines are less commonly used in teaching numerical comparison in the United States as compared to horizontal number lines(e.g., Mosley, 2001).

Given the complexity of price comparison, one way to teach the skill is to break the task into a series of teachable steps(i.e., chained steps) (Sandknop et al., 1992; Storey & Miner, 2011). One common strategy for teaching chained steps tostudents with autism is video-based instruction – an evidence-based intervention based on the criteria (e.g., a minimal offive studies meeting acceptable methodological criteria to permit confidence in findings) proposed by Horner et al. (2005) –to teach functional skills, such as social skills and purchasing (e.g., Bellini & Akullian, 2007; Mason et al., 2013; Sigafoos,O’Reilly, Lancioni, Cannella-Malone, & Edrisinha, 2007).

There are two types of video-based instruction commonly used: video modeling and video prompting (Bellini & Akullian,2007). With video modeling, the whole sequence of chained steps is shown together before a student is asked to perform thetask. With video prompting, students are provided step-by-step visual prompts before given opportunities to perform eachstep (Cannella-Malone et al., 2006). Although there is limited research that directly compares video modeling and videoprompting for students with autism (Rayner, Denholm, & Sigafoos, 2009), video prompting may be more effective in learningcomplex chained steps (Sigafoos, O’Reilly, Cannella, et al., 2007). Video prompting may also reduce a student’s cognitive loadto a greater extent than video modeling (Banda, Dogoe, & Matuszny, 2011; Sweller, Ayres, & Kalyuga, 2011). In addition,video prompting may promote faster acquisition for students with autism (e.g., Cannella-Malone et al., 2006). Cannella-Malone et al. (2006) directly compared video prompting to video modeling and the results showed video prompting yieldedrapid acquisition for five of the six adults with autism spectrum disorders studied.

Although video prompting is an intervention in and of itself, it can also be combined with error correction. Among errorcorrection strategies, the systems of least-to-most (LTM) prompts and most-to-least (MTL) prompts are most commonlyused (Cannella-Malone, Wheaton, Wu, Tullis, & Park, 2012; Mason et al., 2013). Both procedures are effective to facilitatetask acquisition and present their own advantages and disadvantages (Fentress & Lerman, 2012). For example, while MTLprompting is favored in teaching new and complex chained steps and results in fewer errors, it also provides feweropportunities for students to initiate performance (Fentress & Lerman, 2012). To tackle this disadvantage, MTL prompting isoften used with constant time delay (i.e., a fixed amount of time given to students prior to delivering a prompt). Thecombination of two procedures provides students opportunities to respond and results in fewer errors and fast acquisition(Fentress & Lerman, 2012; Libby, Weiss, Bancroft, & Ahearn, 2008). For example, Libby et al. (2008) compared the effects ofMTL prompts with and without constant time delay. The results showed the students with autism performed better underthe condition using MTL prompts than with constant time delay.

Some researchers suggested video prompting was more effective when used in conjunction with error correctionprocedures (e.g., Cannella-Malone et al., 2006; Sigafoos, O’Reilly, Cannella, et al., 2007). However, the effects of combiningerror correction are still inconclusive. A recent meta-analysis by Mason et al. (2013) concluded that for students with autismthe accumulated effect sizes of video-based instruction alone were slightly larger than the accumulated effect sizes of thevideo-based instruction combined with other instructions (e.g., error correction procedures, social stories, and picturecards). Because Mason et al. (2013) did not isolate error correction procedures from other instruction, the effects of videoprompting alone and video prompting combined with error correction still requires investigation.

In addition to the need for further investigation on the effects of combining video prompting with other procedures toteach students with autism, the use of the medium to deliver video prompting requires validation. With the emphasis onteaching functional skills in community settings to promote generalization and independence for students with autism(Cihak & Grim, 2008; Storey & Miner, 2011), the portability of the medium becomes a critical factor (Cannella-Malone et al.,2012). In the beginning, video prompting was less likely to be delivered in a community setting (e.g., a grocery store) becauseit was delivered via a less portable device (e.g., desktop computer, laptop, projector). Alternatively, mobile devices (i.e., iPodor iPad) provided greater advantages for increased independence in the community (Ayres et al., 2013). Researchersdemonstrated the effectiveness of teaching functional skills via video prompting in community settings using mobiledevices, such as an iPod Touch (e.g., Payne, Cannella-Malone, Tullis, & Sabielny, 2012).

Despite the rich literature on video prompting, the evidence is still lacking for students with autism on using videoprompting to teach functional skills, such as price comparison. The purpose of this study was to investigate the effectivenessof using video prompting to teach price comparison to secondary students with autism in simulation classroom settings andnatural settings (i.e., grocery stores). Research questions included (a) is video prompting more effective for teaching studentsto select lowest priced items than their typical teaching approach?, (b) is video prompting effective for teaching the sevencritical price comparison task analysis steps?, and (c) what are students and special education teachers’ perspectives on pricecomparison, the use of video prompting, and the employment of a mobile device?

2. Method

2.1. Participants

Three students with autism participated in this study. All students (a) were diagnosed with autism and mild-to-moderateintellectual disability; (b) were in middle or high school; (c) had IEP goals related to functional mathematics, including pricecomparison; (d) were able to identify natural numbers (0–20); (e) had not yet mastered price comparison; and (f)

P.-L. Weng, E.C. Bouck / Research in Autism Spectrum Disorders 8 (2014) 1405–1415 1407

participated in community-based instruction involving trips to grocery stores. Information on understanding the numericalmagnitude – or lack thereof – of price comparison skills was obtained during interviews with students’ special educationteachers.

2.1.1. Mitch

Mitch was a 17-year-old, eleventh-grade, Caucasian male student with autism. He was identified with a moderateintellectual disability, with a full-scale IQ of 46 based on the Wechsler Intelligence Scale for Children (WISC). His AdaptiveBehavior Assessment System (ABAS) score of 48 indicated a moderate disability (Lichtenberger & Kaufman, 2009). Mitch’sGilliam Autism Rating Scale indicated a high probability of autism based on his teacher’s rating (Autism Index of 91) andparents’ ratings (Autism Index of 113). In terms of functional mathematics, his special education teacher reported that Mitch(a) demonstrated numerical comparison for numbers up to 20, (b) could tell price numbers but had difficultiesdifferentiating between sale prices and regular prices, and (c) was able to use the next dollar strategy with minimal verbalprompting. Mitch was routine-oriented and had a limited attention span on tasks.

2.1.2. Manny

Manny was a 15-year-old, eighth-grade, Hispanic male student with autism. He was identified with a mild intellectualdisability (a Full Scale IQ score of 57 based on an Universal Nonverbal Intelligence Test [UNIT, an intellectual ability testrequired no receptive or expressive language; McCallum & Bracken, 2012] and a Leiter IQ score of 61). However, his ABASscore of 40 indicated a moderate disability. Manny was identified with mild to moderate autism based on the ChildhoodAutism Rating Scale (CARS). In terms of functional mathematics, his special education teacher reported that Manny (a) couldwrite numbers up to 20, (b) had not yet mastered the relationship between numbers and quantities, and (c) could locategrocery items on a shelf in grocery stores. Additionally, Manny was an augmentative and alternative communication user. Herequired step-by-step verbal prompting from adults throughout the day.

2.1.3. Leo

Leo was a 15-year-old, eighth-grade, Caucasian male student with autism. IQ scores were not reported in his schoolpsychological report; the Differential Ability Scales (DAS) was administered but not completed. The results of threecompleted subtests of the DAS – block building, picture similarities, and copying abilities – placed Leo’s scores two standarddeviations below the means. The psychologist concluded that Leo’s cognitive functioning might be impaired. Leo’s ABASscore of 40 indicated a moderate disability. Leo met eligibility criteria for Autism Spectrum Disorder (511 IAC 7-41-1) underthe Indiana State Board of Education Special Education Rules (Article 7, Rule 41); no autism scale score existed in Leo’s file. Interms of functional mathematics, Leo’s special education teacher reported he could identify values of coins and paper billsand practiced comparing prices when shopping. Leo was capable of performing most daily tasks; however, at times herefused to participate.

2.2. Settings

This study took place in two settings: students’ special education classrooms and local grocery stores. Althoughcommunity-based instructions are preferred (Storey & Miner, 2011), a simulated setting – providing a controlledenvironment to minimize potential confounding variables – was employed, in light of the fact that this study was the firstattempt to examine the effectiveness of video prompting in teaching price comparison. Interviews as well as baseline andintervention data collection occurred in students’ classrooms and generalization occurred in grocery stores where eachstudent practiced his community-based activities.

2.2.1. Classrooms

In Mitch’s classroom, the grocery simulation occurred in the kitchen area adjacent to the classroom. The researchersarranged five chairs in a row; each chair displayed three different brands of the same product from right to left. Thekitchen door was closed during the sessions to minimize distractions from other classroom activities. In Manny’sclassroom, the grocery simulation was set up on a large U-shaped desk located in the back of the classroom. Researchersplaced the five groups of items on the desk from right to left. Leo’s classroom consisted of two different sized roomsadjacent to each other – the smaller of the classrooms was used during the study. The researchers placed all items on oneof the larger desks.

2.2.2. Grocery stores

Mitch and Leo went to the same grocery store for community-based activities. For Mitch, the grocery store was locatedone block (a 5-minute bus ride) from his school. For Leo, the grocery store was a 20-min bus ride from his school. Mannywent to a different grocery store, which was a 15-min bus ride from his school. Both grocery stores had similar layouts, withdeli, meat, produce, and dairy sections located around the margins of the stores, and aisles in the middle of the store. Bothstores were brightly lit environments with a slow pace and a friendly atmosphere. As is commonly found, prices tags foritems were placed on shelves under grocery items. Price tags contained multiple numbers and information, such as brandnames, barcodes, print dates, and price numbers – in a relatively large font size.

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2.3. Materials

2.3.1. Video prompting clips and device

A Sony Nex-5 video camera was used to record the 18 task analysis steps demonstrated by the researcher to serve as thevideo prompting clips. The video was recorded in a simulated setting using the first-person perspective. Three items – allfeaturing one product – (i.e., toothpaste: Colgate1, Sensodyne1, Crest1) were placed on the desk and their associated pricedtags (i.e., $3.59, $11.99, $5.59) were placed in front of each item. iMovie was used to combine the clips and audio cuesgenerated using NaturalReader software. The 18 separate clips were embedded into an Apple iBook format, in which eachpage contained one step of a video prompting clip (ranging in duration from 7 to 16 s) and audio cues. To navigate, thestudents slid through each page with their fingers and pushed the play button in the middle of the screen. An Apple iPad 21

with a protective case that could convert into a stand when viewing video prompting was used as the medium to delivervideo prompting.

2.3.2. Grocery items

A total of 34 grocery products were suggested by the special education teachers, which represented commonly purchaseditems during community-based activities. For each of the 34 products (e.g., toothpaste), researchers purchased threedifferent brands (e.g., Crest1, Colgate1, Sensodyne1), resulting in 102 grocery items (34� 3) to be used during baseline andintervention. Each item was labeled with a price tag (200 � 100) containing a dollar sign and one or two digits followed by adecimal point and two digits (e.g., $3.59 and $2.99). All three students shared the same 102 items. Throughout the entirestudy, each individual product was repeatedly presented in two to four different sessions. In addition, no same five groups ofitems were repeated throughout baseline and intervention sessions. During generalization, each student was given targetproducts from his class’s weekly shopping list.

2.3.3. Other items

Other materials included an eraser, marker, shopping basket, and number line. In this study, we used an 1800 � 200, paper-based, horizontal number line, containing the numbers 0–19 arranged from left to right. The physical size of the numbers onthe number line were congruent with the number magnitudes with font sizes ranging from 14 to 50, to present the relativenumerical magnitudes based on the size congruity effects theory (Henik & Tzelgov, 1982). The number line was laminated sothat students could apply dry-erase markers to it repeatedly.

2.4. Task analysis

For intervention and generalization phases, an 18-step task analysis for selecting the lowest-priced item out of threeitems was developed based on the task analysis by Sandknop et al. (1992), as well as the steps required to employ thenumber line to select the smallest numbers. The 18 steps include: (a) pick up the number line, marker, eraser, and basket; (b)open the number line and put it on the desk; (c) point to the first item and tag; (d) cover digits after decimal with fingers; (e)move the number line closer to tag to match numbers; (f) circle the number on the number line; (g) point to the second itemand tag; (h) cover digits after decimal with fingers; (i) move the number line closer to tag to match numbers; (j) circle thenumber on the number line; (k) point to the third item and tag; (l) cover digits after decimal with fingers; (m) move thenumber line closer to tag to match numbers; (n) circle number on the number line; (o) point to or say which circled numberis closest to 0; (p) match the number on the number line with tag; (q) pick up the lowest-priced item and place it in thebasket; and (r) erase circles. In this study, students were only required to read the numbers before decimals, and the numberswere different in each item per trial. The first step was not included during the generalization sessions because the itemswere given to students in the grocery store.

Of all the 18 steps, seven were deemed critical – meaning if missed, students would not be able to continue the task usingthe number line as a visual cue – by the researchers and the special education teachers whose students participated in thestudy. For example, from the sixth treatment session onward, Mitch started to skip the steps ‘‘cover digits after decimal withfingers’’ and ‘‘move the number line closer to tag to match numbers,’’ but was still able to perform the step ‘‘circle the numberon the number line’’ independently. The seven critical steps included: (a) open the adapted number line and put the numberline on the desk, (b) circle the first number on the number line, (d) circle the second number on the number line, (e) circle thethird number on the number line, (f) compare the three circles and point to or say which number is closest to 0, (g) pick upthe lowest-priced item and place item in the basket, and (h) erase circles.

2.5. Dependent and independent variables

One dependent variable was measured in this study: the mean percent of the lowest-priced grocery item independentlyselected per session for baseline, treatment, and generalization. In addition, we analyzed the mean percent of the sevencritical task analysis steps independently completed per session for treatment and generalization. In this study, theindependent variable was video prompting. During intervention and generalization sessions, video prompting wasemployed to teach price comparison. The effects were compared with the traditional means (e.g., adult verbal direction) usedduring the baseline sessions.

P.-L. Weng, E.C. Bouck / Research in Autism Spectrum Disorders 8 (2014) 1405–1415 1409

2.6. Experimental design

A single-subject experimental design – using a multiprobe, multiple baseline design across participants (Kennedy, 2005) – wasimplemented to examine the effect of video prompting on teaching price-comparison. This design allowed researchers to (a)analyze the effectiveness of the intervention based on the discrepancy between baseline and intervention data across participants,and (b) intermittently collect baseline data (Kennedy, 2005). The first author, a doctoral student of special education who hasseveral years of experience working with students with autism, served as the interventionist and the trainer of second observers.

2.6.1. Baseline

Each baseline session consisted of five trials. For each session, five different grocery products were randomly selectedfrom the 34 groups of products. The five products – each of which included three items – were arranged in a row on a table orchairs. Each student individually entered the structured simulation setting located in his classroom. Students were givenverbal directions (e.g., ‘‘Pick up the cheapest item and put it in the basket’’) to select the lowest-priced item from the givenchoices per group. The directions were similar to what students typically received from teachers in a grocery store; noadditional instruction was provided. Responses were counted as correct if a student completed the tasks of picking thelowest-priced item and placing it in the basket within 20 s. The responses were considered incorrect if students did not pickup the lowest-priced item and place it in the basket, or did not respond within 20 s.

2.6.2. Intervention

During the intervention sessions, the procedure was similar to the baseline: five trials per session and three brands perproduct/trial with the same setting and selection procedure for grocery products. However, during intervention sessions, theresearcher set the iPad on a table (when working with Manny and Leo) or a counter space (when working with Mitch) in front ofstudents. Students would place the number line on the surface (e.g., table) in front of the products following the video prompt.Students watched the video prompting prior to performing each step. When sessions started, the researcher gave the sameverbal direction as the baseline, in addition to asking students to watch videos. After this initial verbal direction, no other adultcorrection was provided under the video prompting only condition. The researcher then played the first video prompting clip onan iPad. After they had finished viewing each clip, students were given 20 s to complete the step during video prompting aloneintervention sessions. Similar to the baseline, the responses were considered correct if students accurately completed the targetstep within 20 s. The responses were considered incorrect if the students did not respond within 20 s or responded incompletelyor incorrectly. After 20 s, regardless of a correct or incorrect response, students were shown the next step via video prompting.Each student was shown a total of 18 video clips of price comparison task analysis step by step.

Because the literature is inconsistent regarding the effectiveness of video prompting in comparison to video promptingwith MTL prompting (Mason et al., 2013), all students underwent video prompting first. If a student did not benefit (i.e.,independent completion of 50% of critical task analysis steps) from video prompting during 10 trials (i.e., two sessions), MTLprompting was added in addition to video prompting (Cannella-Malone et al., 2012). A four-level MTL prompt hierarchyprovided by the researcher included: (a) hand-over-hand assistance with verbal cues, (b) model with verbal cues, (c) gesturewith verbal cues, and (d) verbal cues only (Wolery, Ault, & Doyle, 1992). A 5-s constant time delay procedure was providedafter showing each clip and before applying an MTL prompt (Wolery et al., 1992).

2.6.3. Generalization

During generalization sessions, only three trials per session were conducted due to time constraints within thecommunity-based setting. The procedures were similar to the intervention phase; however, data were collected in a grocerystore. Students were required to pick up the lowest-priced items from three choices on shelves. The researcher arrived at thegrocery store 20 min before each student and chose three items with different prices for each product. The researcher chosethe items with regular price tags and avoided the price tags with promotional signs, such as two for $5.00. Once a sessionstarted, the researcher took each student to the target sections/aisles for each of the three trials and held the iPad throughoutthe session. Prior to each trial, the researcher used fingers to point to the three price numbers of the three previously-selectedproducts to the student. The students were then asked to choose the lowest-priced item with the same procedures usedduring intervention (i.e., video prompting or video prompting with MTL prompting). Researchers created a surface forstudents to use the number line in the grocery story by holding a clipboard.

2.7. Interobserver agreement and treatment integrity

Interobserver agreement was collected for 23.3% of all sessions for three students across baseline and interventionphases. Two special education teachers and two paraprofessionals took turns serving as the second raters. The interobserveragreement for selecting the lowest-priced item and for completing the task-analysis steps was calculated by the number ofagreements divided by the sum of the number of agreements and disagreements. The interobserver agreement was 100% forMitch, 92% for Manny, and 100% for Leo. A checklist was used to assess treatment integrity, including randomizing groceryitems, setting up the simulated environment, maintaining environmental control, establishing video prompting procedures,applying constant time delay, and providing correct levels of MTL prompts. Treatment integrity was collected for 37.5% of theintervention sessions for all students. Treatment integrity was 100% for Mitch, 95.6% for Manny, and 97.8% for Leo.

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2.8. Social validity

A social validity assessment was conducted to solicit feedback from the three students and three special education teachersregarding the intervention procedures. Students and their special education teachers were interviewed before and after thestudy regarding price comparison, video prompting, and the use of mobile devices such as iPads as the medium of contentdelivery. Special education teachers were interviewed regarding how price comparison was taught in their community-basedactivities. The three students were interviewed one-on-one; questions asked were verbally supplemented with pictures. Mitchand Leo answered questions verbally or by pointing, while Manny used his Dynavox device or pointed to pictures.

2.9. Data analysis

Results were interpreted through visual analysis of the graphed data from the baseline, intervention, and generalizationphases. The following data were calculated: (a) the mean and range of percent of independent selection of the lowest-priceditems for the baseline, intervention, and generalization phases; and (b) students’ mean and range of independentlycompleted task analysis steps for the intervention and generalization phases. In addition, percentages of non-overlappingdata (PND) were calculated using that portion of the data points exceeding the highest point of the baseline within theintervention phase (Scruggs & Mastropieri, 1998).

3. Results

The visual analysis revealed an apparent effect from using video prompting to teach price comparison steps for two of thethree students – one of the two required MTL prompts. Fig. 1 depicts the percent of selected the lowest-priced itemsindependently during baseline, intervention, and generalization across the three students. Fig. 2 demonstrates the percent ofindependently completed critical task analysis steps during intervention and generalization across the three students. Therewere no data points in the baseline phase in which the task analysis was not applicable. The results for each task analysis stepare presented at the bottom of Fig. 2.

3.1. Mitch

During baseline, Mitch’s average for independently selecting the lowest-priced item was 25% (range 0–60%) (see Fig. 1).During intervention, Mitch increased to a mean of 77.5% (range 60–100%) of independent selection of the lowest-priceditems. The PND between baseline and intervention was 75%, indicating an effective effect (Scruggs & Mastropieri, 1998).During generalization, Mitch selected the lowest-priced item an average of 67.0% of the time. During intervention, Mitchunderwent only one condition: video prompting. After 10 trials (i.e., the first two intervention sessions) using videoprompting, Mitch independently completed an average of 90% of critical task analysis steps (see Fig. 2). Hence, Mitch onlyused video prompting throughout the intervention and generalization phases. During intervention, Mitch independentlycompleted, on average, 94.6% of the critical task analysis steps. During generalization, Mitch independently completed anaverage of 89.0% of the critical analysis task steps.

3.2. Manny

During baseline, Manny’s mean for independently selecting the lowest-priced item was 40% (see Fig. 1). However, the resultsduring the baseline were likely due to chance because Manny chose items located in the very far left for every trial duringsession 1 and items located in very far right for every trial during the reminder of the phase. During intervention, Mannyunderwent two conditions: video prompting alone and video prompting with MTL prompts. With just video prompting, Mannynever selected the lowest-priced item. The PND between baseline and video prompting alone was 0%, indicating no effect(Scruggs & Mastropieri, 1998). With video prompting in conjunction with MTL prompts, Manny independently selected thelowest-priced item with an average of 16.7% accuracy. The PND between baseline and video prompting with MTL was also 0%,suggesting an ineffective intervention (Scruggs & Mastropieri, 1998). During generalization, Manny selected the lowest-priceditems with a 50% mean (range 33–67%). After 10 trials of using video prompting alone during intervention (representingManny’s first two intervention sessions), Manny independently completed an average of 27.2% of critical task analysis steps (seeFig. 2). Hence, Manny failed to reach the preset criterion. With video modeling in conjunction with MTL prompting sessions(sessions 13–18), Manny independently completed, on average, 66.2% of the critical task analysis steps. Due to the ending of theschool year, Manny’s intervention phase was terminated before reaching 80% accuracy in selecting the lowest-priced items.During generalization, Manny independently completed the critical task analysis steps with a mean of 75.0% (range 72–78%).

3.3. Leo

During baseline, Leo’s mean for independently selecting the lowest-priced items was 20% (range 0–40%) (see Fig. 1).During intervention, Leo underwent video prompting alone and video prompting with MTL prompts. With video promptingalone, Leo independently selected the lowest-priced item with an average of 40% accuracy. The PND between baseline and

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Manny chose the items placed on the far right side of each group throughout five trials.

P.-L. Weng, E.C. Bouck / Research in Autism Spectrum Disorders 8 (2014) 1405–1415 1411

video prompting alone was 0%, suggesting an ineffective intervention (Scruggs & Mastropieri, 1998). With video promptingin conjunction with the MTL prompting, Leo’s mean of independently selecting the lowest-priced items increased to 96.7%(range 80–100%). The PND between baseline and video prompting with MTL was 100%, suggesting a large effect (Scruggs &Mastropieri, 1998). Due to a scheduling conflict, Leo only completed one generalization probe. He independently selected100% of the lowest-priced items. After 10 trials of using video prompting alone, Leo independently completed, on average,22.9% of critical task analysis steps (see Fig. 2). Leo failed to reach the preset criterion; video prompting with MTL promptingwas administered for the remainder of the sessions. During the video prompting in conjunction with MTL promptingcondition, Leo independently completed the critical task analysis steps with an average of 75.7% (range 45.7–91.4%). Duringthe generalization phase, Leo independently completed 83.5% of the critical task analysis steps.

3.4. Social validity

During the pre-intervention interview, the three teachers agreed that teaching price comparison was an importantfunctional skill because it can facilitate independent living. All three teachers taught price comparison skills to their studentsby providing verbal direction (e.g., ‘‘which one is cheaper?’’) in grocery stores. One teacher mentioned one advantage oflearning price comparison is that it helps students to budget the limited funding available to them. All teachers expressedpositive opinions about the idea of using video prompting to teach new skills, despite limited experience. They liked the ideaof using iPads as a means to present video prompting, given its portability and potential for use with community-basedactivities. During the post-intervention interview, all three students expressed positive opinions on learning skills from a

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1 80 80 60 100 60 100 100 100 ^ ^2 0 0 20 100 100 100 100 100 100 1003 0 0 20 80 100 100 100 100 100 1004 20 0 40 100 100 100 100 100 100 1005 0 0 0 0 0 0 0 0 0 06 0 0 0 0 0 40 0 60 33 677 100 100 100 100 100 100 100 100 100 100

Leo

1 40 0 0 60 80 60 100 100 ^ –2 0 0 60 60 80 60 80 100 100 –3 0 0 40 80 100 80 80 100 67 –4 0 0 60 80 100 100 80 100 100 –5 0 0 0 40 20 60 40 40 67 –6 40 40 80 100 100 100 100 100 100 –7 100 100 80 100 100 80 100 100 67 –

20406080

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

20406080

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

20406080

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Manny

Leo

Mitch

Baseline GeneralizationIntervention

Video promptingVideo prompting +MTL prompt

Perc

enta

ge c

orre

ct

Fig. 2. Percent of independently completing critical steps of price comparison. Notes: Non-italic numbers: video prompting only. Italic numbers: video

prompting with MTL prompts. *Steps: 1. Open the number line and put the number line on the desk; 2. Circle number on the number line; 3. Circle number

on the number line; 4. Circle number on the number line; 5. (Compare 3 circles) Point to or say which number close to 0; 6. Pick up the cheapest item and

place it in the basket; 7. Erase circles. ^, step not applicable in generalization. –, data not collected.

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video on an iPad. Regarding video prompting, one teacher appreciated the fact that the video was broken down into steps,but thought that the computer-generated sounds might be hard to understand at times. Although the teachers liked the ideaof teaching price comparison using video prompting, one teacher expressed concern that it may be too individualized to becreated. When asked about the adapted number line, Mitch and Leo indicated they would use the number line in the future,and one teacher commented, ‘‘The adapted number line was genius. I loved that the numbers progressively got larger on thenumber line.’’ However, she indicated that the size of the number line should be smaller for community-based activities.

4. Discussion

This study examined the effectiveness of employing video prompting to teach price comparison to students with autism.Video prompting was found to be an effective strategy for teaching price comparison using an adaptive number line to two ofthe students; the third student appeared not to benefit much from the intervention. Based on the results, the study failed toshow consistent effectiveness, the authors scrutinized individual results and student characteristics in order to unfoldpotential factors.

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4.1. What works for whom

The results of two of the students in the current study support previous findings in the literature demonstrating theeffectiveness of video prompting to teach functional skills for students with autism (e.g., Cannella-Malone et al., 2006). Yetvideo prompting alone was not always sufficient; Manny and Leo required MTL prompts in addition to video prompting.These mixed findings were actually anticipated based on previous literature, indicating mixed results for video promptingalone versus video prompting with error correction. In addition, it was not surprising given the heterogeneous nature ofautism that students required different levels of interventions (Iovanne, Dunlap, Huber, & Kincaid, 2003). For example, asdescribed by Manny’s special education teacher, Manny was prompt dependent, especially during learning new skills. Thiscould possibly explain why Manny was unable to acquire steps in a video-prompting alone situation initially, but he was ableto perform five out of seven steps independently using video prompting from the sixth intervention session. Our results areconsistent with some of the previous studies on video-based instruction, which demonstrated that the majority of studentsin previous single-subject design studies benefit more from the combination of video-based instruction and error correctionthan video-based instruction alone (e.g., Cannella-Malone et al., 2006; Sigafoos, O’Reilly, Cannella, et al., 2007). AlthoughMitch was able to master price comparison steps under the video-prompting alone condition, the authors hypothesize thatthe acquisition rate may have increased under the combination of video prompting and MTL prompts if that interventionpackaged were implemented.

4.2. What works in what context

Despite the overall effectiveness of video prompting to teach the chained steps of price comparison, video prompting wasless effective in teaching the numerical comparison step (i.e., selecting the circled number close to zero, meaning thesmallest number). As compared with other functional skills (e.g., washing dishes), price comparison is a more difficult task,including steps needing different levels of cognition involvement (Sandknop et al., 1992). Therefore, it is important toscrutinize each step of the task analysis instead of analyzing all steps holistically in order to discern critical informationregarding video prompting. From this scrutiny, it appeared that video prompting was more effective in teaching some stepsthan others. For example, the data of task analysis steps indicate Manny and Leo demonstrated the slowest acquisition ratefor the numerical comparison step. Manny failed to complete this step independently during intervention andgeneralization. In addition, Leo only reached 67% independent completion for the numerical comparison step, whencompared with the other six steps in which Leo reached 100% completion.

For the numerical comparison step, students were required to make their own judgment based on presented tasks andcould not simply imitate what was shown on the clip. In other words, the numerical comparison step might require a higherlevel of cognition involvement as compared to other steps (e.g., ‘‘erase circles on the number lines’’) that can be performedvia pure imitation. The data on critical task analysis steps completed across three students indicate the steps requiring thelowest cognition involvement (i.e., pure imitation), such as ‘‘erase circle’’ had the fastest acquisition rate, followed by – fromthe faster to the slower acquisition rate – ‘‘open the number line and put the number line on the desk’’, ‘‘circle numbers onthe number lines’’, ‘‘pick up the cheapest item and place it in the basket’’, and ‘‘compare three circled numbers and point to/say the number that is the closest to zero’’. This study also added to the literature on using mobile devices to deliver videoprompting for students with autism. Mobile devices – typically lightweight with a long battery life – can be easilytransported across different settings. Hence, mobile devices make teaching skills in community settings more feasible, whichsupports the value of teaching students with autism and intellectual disability in actual settings as opposed to simulations aswere done in this study.

4.3. Implications for practice

Video prompting can be an effective way to teach functional skills step by step. However, video prompting does not allowstudents to perform a whole chained task in a natural way without interruption (Sigafoos, O’Reilly, Cannella, et al., 2007). Toensure students’ independence in being able to perform a sequence of steps, educators need to gradually fade videoprompting after students acquire each step. One approach to fade video prompting is to decrease the amount of videoprompting by gradually combining video prompting clips (e.g., Sigafoos, O’Reilly, Cannella, et al., 2007). Another approach isto retain audio prompts and fade the reliance on visual prompts (i.e., videos), such as converting videos from a moresophisticated device to a more cost efficient device, such as a digital audio recorder (e.g., Bouck, Satsangi, Bartlett, & Weng,2012). This approach of fading video prompting to a more cost efficient device is to consider the possibility of some studentsbeing unable to access a mobile tablet outside school or after graduation.

Second, although video prompting is an evidence-based practice (Sigafoos, O’Reilly, Lancioni, et al., 2007), creating videoprompting can be time consuming for school practitioners, as indicated by one participating teacher. One way to tackle thisissue is through collaboration with other practitioners. Today, teachers commonly share instructional materials online(Bichelmeyer & Molenda, 2006). Likewise, teachers can create a repository to store and share their video-based instructionclips on a secure online space, such as a non-public shared drive (e.g., school shared drives), a public shared drive (e.g., GoogleDrive or Google Sites), or a public channel (e.g., YouTube) with a secure privacy setup. Additionally, if students were in videoclips, teachers need to be aware of the legal procedures (e.g., consent forms) before sharing with others. If sharing videos

P.-L. Weng, E.C. Bouck / Research in Autism Spectrum Disorders 8 (2014) 1405–14151414

containing images of students is a concern, practitioners can use adults as a model. Several studies showed using an adult as amodel was as effective as using students (Bellini & Akullian, 2007).

4.4. Limitations and future directions

Several limitations of this study may affect the overall results and interpretations. First, the study was administered tothree students from three different schools, which generated confounding variables related to environmental factors. Forexample, Manny performed his tasks in his home classroom where lectures or activities occurred simultaneously. He wasoften distracted and appeared to be anxious during whole-class activities. Future research should minimize such factors byproviding a space with limited distraction. Second, only four data points were collected during Mitch’s baseline. For futureresearch, it is recommended to have at least five data points within a phase (Kratochwill et al., 2013). Third, the length andnumber of sessions of the study was constrained by the availability of students, particularly as the school year was coming toan end. The study failed to demonstrate a stable baseline for Mitch prior to his intervention phase, and Manny’s interventionphase was terminated prematurely before his generalization phase. Related, due to a time limit for shopping in grocerystores, students were only required to complete three trials per generalization probe, whereas they were given five trials perintervention probe. Future studies should involve an equal number of trials for each session. Furthermore, during baselineand intervention, all three students shopped for the same 102 grocery items, yet, during generalization, grocery items to beselected in grocery stores were based on individual class needs. Therefore, students selected from different sets of groceryitems, which may have lacked equivalency. Finally, future research should include the generalization probes and provideopportunities for students to perform task analysis steps during baseline to demonstrate a functional relationship.

Beyond improvements to the study during replications, future research is needed on how to teach the steps that requirehigher levels of cognitive involvement within price comparison tasks (e.g., the numerical comparison step) in videoprompting. One student imitated exactly what the adult model was doing in a clip with regards to circling the numbers forprice comparison, instead of applying the concept of choosing the lowest-priced items based on the given tasks. Futureresearch should look into eliminating imitation effects for these steps. Students could practice using the same productsshown on clips until they learn all task analysis steps before generalizing to other products. Second, the study showednumerical comparison is a prerequisite step for price comparison and would be difficult to teach simply through modeling invideo prompting. Future studies should compare the effect of teaching numerical comparison in a training session prior toteaching the chained steps of comparing prices. Next, number lines need to be more portable based on observation andsuggestions of authors and participating teachers. It would be difficult for students to become independent shoppers if theywere required to simultaneously handle many items (e.g., a portable device, number line, eraser, and marker) while groceryshopping. To enhance independent living and learning, future research needs to explore the use portable media to presentnumber lines. Last, this study only taught students to choose lower-priced items. As discussed in Sandknop et al. (1992),selecting lowest-priced items may not be always the best choice; therefore, future research should look into other factorsinvolving price comparison such as quality, quantity, and individual preference of a product.

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