etadd_47(3) - Division on Autism and Developmental Disabilities

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implement AT with which they themselves have little familiarity. Therefore, adapting a device like an iPod or iPhone that has widespread usage within the general population is a logical step. Thus, beginning to train individuals to use a handheld device (e.g., an iPod or iPhone) to take advantage of the flexible range of software supports becomes the next challenge. Video modeling on computers, handheld devices, and other tools has been used across disciplines and across settings to teach a variety of skills. In 2002, Davies, Stock, and Wehmeyer examined the use of a handheld, selfdirected visual and auditory prompting system to improve the independent performance of community-based vocational tasks. In addition, the researchers tried to determine if the use of the handheld systems would also reduce the amount of support needed from teachers or job coaches. They reported that the handheld prompting systems resulted in increases for each student’s independent functioning in community-based tasks, but more importantly, the study demonstrated the handheld systems reduced the amount of support time required by the teacher or job coach for each student. In two studies, one with middle school aged students with moderate intellectual disability (Hammond et al., 2010) and the second with elementary-aged students with autism (Hammond, Muething, Ayres, & Gast, in review), researchers examined the use of video modeling to teach students to access activities on iPods. The intention was, in part, to evaluate the use of video modeling with fine motor tasks that may be more difficult for users to discriminate. In both cases, all students learned the target tasks, but important limitations were identified in the initial study (Hammond et al., 2010). These researchers used single opportunity probes (Cooper, Heron, & Heward, 2007) to evaluate student performance of the steps of their task analyses (e.g., accessing music, accessing videos, and accessing photographs). This may have artificially suppressed baseline responding because if a student failed at the first step, they would not have had a chance to demonstrate whether they could perform subsequent steps. While the students’ mastered the skills, their performance deteriorated over time. During main- tenance probes, the researchers noted the lower levels of responding and implemented “booster” sessions of video modeling to help the students regain criterion level performance. In a subsequent study, Hammond et al. (in review) used the same instructional methodology and incorporated multiple opportunity probes rather than single opportunity probes. This allowed participants to make an error on a step and have a chance to respond to the later steps in the task analysis (TA). The effects of intervention were evaluated with a multiple baseline design across behaviors replicated across participants. After learning one of the target skills, two of the participants generalized those steps to the other tasks that shared many of the same procedural steps. As a result of the design’s structure, this generalization did not weaken Hammond et al.’s (2010) experimental control. However, it did suggest that the trio of behaviors being taught may have shared too many critical features to serve as target behaviors in this research context where ideally you would want skills that are functionally independent to safeguard against carry-over threats (Wolery, Gast, & Hammond, 2010). The current study is a replication and extension of Hammond et al. (in review). Capitalizing on the foundation laid with the structure of the research design, this study used the same multiple baseline design across behaviors replicated across participants to evaluate the effects of video modeling on student acquisition of three skills on an iPhone 3GS. The purpose of the replication is to apply a similar procedure to a more complex device and evaluate generalization to untrained interfaces. The research questions addressed here include: (1) What effect does video modeling have on the acquisition of taking a photograph of a person, viewing photographs in a slideshow, and viewing a video in iTunes?; and (2) If students acquire the skills via video modeling in an environment of reduced distracters (e.g., only the minimum number of buttons on the screen) will the student generalize this without training to an environment filled with distracters (e.g., screen filled with icons)? 320 / Education and Training in Autism and Developmental Disabilities-September 2012
Method Participants Four high school students, all identified as having moderate intellectual disability (MOID), initially agreed to participate in the study. However, due to health concerns and multiple absences, the fourth participant had to leave the study. All participants were selected because they had IEP goals related to recreation and leisure that fit with the target objectives of the project (e.g., visual discrimination and following instructions). Participants additionally possessed the following prerequisite skills: (a) visual acuity and manual dexterity sufficient to place the tip of a finger ona1cmby1cmtarget on the face of an iPhone; and (b) ability to sit for at least 15 minutes. The first participant, Holly, was a loquacious 21 year and 5 month old female at the onset of the study with a diagnosis of Down’s Syndrome and MOID. Holly displayed deficits in reading for informational purposes and counting mixed dollar bills. However, Holly possessed strengths in learning to expressively state sight words, sound out unfamiliar words, and her ability to manage personal items (e.g., purse, insulin materials, and cell phone). Prior to the study, Holly had experience using a family member’s iPhone 3GS to send and receive text messages. However, Holly’s performance was dependent upon assistance with reading and typing messages on the iPhone. The Vineland Adaptive Behavior Scales (VABS; Sparrow, Balla, Cicchetti, & Doll, 1984) indicated that she showed standard scores of 63 for daily living, 86 for socialization, and 62 for communication with a composite of 68. The Differential Ability Scales (DAS; Elliott, 2005) indicated that Holly scored a <strong>47</strong> in the verbal cluster, a 57 in the nonverbal reasoning cluster, 50 in the spatial cluster, a <strong>47</strong> in the general cognitive ability, and a 51 in the special nonverbal composite. Jake, the second participant, was 17 years and 9 months old at the introduction of the study with a diagnosis of Fragile X and MOID. Jake displayed anxiety and attention-seeking behaviors (e.g., hand-biting, using inappropriate language). This behavior was avoided throughout the study via verbal reassurances and redirection along with sustained involvement. Jake displayed deficits in attention and impulse control, oftentimes negatively impacting his ability to acquire new information and skills. Jake possessed strengths in computer and calculator proficiency. The DAS (Elliott, 2005) indicated that Jake scored a 76 in the verbal cluster, a 53 in the nonverbal reasoning cluster, and a 61 for the overall general cognitive ability score. The final participant, Norman, was 18 years old at the beginning of the study. He displayed deficits in adaptive behavior that impacted his educational progress as he tended to be easily distracted. Norman’s strengths included his ability to recall key parts of stories that were read to him and his ability to learn and recall sight words. Norman also enjoyed cooking with his class. On the VABS (Elliott, 2005) he scored standard scores of 62 for daily living, 66 for socialization, and <strong>47</strong> for communication with a composite of 56. Results from the Wechsler Intelligence Scale for Children – Third Edition (Wechsler, 1991) indicated a verbal intelligence quotient (IQ) of 54, performance IQ of 46, and a full scale IQ of 46. Materials and Settings Students viewed video models on a 15” Mac- Book Pro laptop. The videos were viewed in QuickTime (QuickTime, 2011) and were cued for the student prior to the session’s commencement. Videos depicted the hands of an actor using an 8 GB iPhone 3GS running iOS 4. The duration of the three video models varied: 28 s for watching a video, 18 s for taking a photograph of a person, and 26 s for looking at photographs. During baseline and initial instruction, the iPhone screen only displayed four soft buttons at the bottom of the screen that included one of the target buttons, the orange iPod button. Soft buttons are those that could not be changed and were located on the bottommost horizontal toolbar. The rest of the screen contained only two icons: one to initiate the iPhone’s camera and one to view the photograph album. Five high interest photographs were included in the photograph album and, similarly, for watching a video, five high interest videos were imported into iTunes. Due to one participant, Jake’s, changing preference throughout the study, a iPhone Usage and Students with MOID / 321
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Education and Training in Autism an
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ities. Scholars have argued that do
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discussions were held with 12 addit
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TABLE 1 A Sample of Community Funds
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et al., 2007). At the school, stude
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students opportunities to work and
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Browder, D. M., & Cooper-Duffy, K.
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Page 21 and 22: of words, and in the short term wer
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Page 29 and 30: erate to severe disabilities. Excep
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Page 33 and 34: TABLE 1 Target Vocabulary Words by
Page 35 and 36: Figure 1. Example of PowerPoint Sli
Page 37 and 38: TABLE 3 Mean Percentage of Correct
Page 39 and 40: Figure 4. Jake’s percentages corr
Page 41 and 42: man-Martin et al. (2005) by demonst
Page 45 and 46: used in the study that did not foll
Page 47 and 48: lected. Stories were read in a quie
Page 49 and 50: y determining the number of interva
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Page 53 and 54: Accordingly, recent research has in
Page 55 and 56: Method Participants Four male child
Page 57 and 58: TABLE 2 Play Scale - Developmental
Page 59 and 60: elated utterances, which were used
Page 61 and 62: Figure 1. Ian’s Progress in Play
Page 63 and 64: Figure 3. Ryan’s Progress in Play
Page 65 and 66: Figure 5. Social Validity: Average
Page 67 and 68: (2000). A comparison of video model
Page 69: Education and Training in Autism an
Page 73 and 74: TABLE 1 Task Analyses for all Tasks
Page 75 and 76: Figure 1. Holly multiple baseline g
Page 77 and 78: Figure 3. Norman multiple baseline
Page 79 and 80: ing at photographs. Jake only requi
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Page 83 and 84: table video aids may lead to greate
Page 85 and 86: TABLE 2 Task Analyses Including Vid
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Page 89 and 90: Figure 2. Percent of steps with err
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Page 97 and 98: TABLE 2 Participants Demographic Da
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Page 101 and 102: TABLE 4 Total Number of Items Answe
Page 103 and 104: Figure 2. Total number of procedura
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Page 107 and 108: and quasi-experimental designs for
Page 111 and 112: & Cobb, 2002). Generalization, the
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Page 115 and 116: Procedure Baseline. In this multipl
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Boning, R. A. (1997). SRA Specific
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TABLE 1 Student demographic informa
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ping, dining out, voting) and diffe
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TABLE 2 Content Standards, Alternat
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Student Achievement Figures 3-6 pro
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Figure 4. Student data across math
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Figure 6. Student data across math
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A second limitation of the current
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y students with intellectual disabi
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TABLE 1 Studies of Mathematics Inte
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subject design to provide an interv
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Neef and colleagues (2003) demonstr
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abilities. Education and Training i
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