Date of Award
12-2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Behavioral Analysis
First Advisor
Ada Celeste Harvey
Second Advisor
Frank Webbe
Third Advisor
Darby Proctor
Fourth Advisor
Mary Beth Kenkel
Abstract
For many children diagnosed with Autism Spectrum Disorder, learning emergency information such as their parents’ names, contact information, or important health information may be useful in case of elopement, getting lost, or abduction. In the present study, I evaluated the use of video self-modeling (VSM) to facilitate acquisition of personal information in comparison to echoic prompts using a parallel treatments design with children with ASD. Components of the intervention included: a test, followed by the prompt (e.g., echoic or VSM) for no response given or for an incorrect response. The use of VSM involves the learner viewing a video of oneself successfully demonstrating a target behavior with the goal of the learner engaging in the correct response in the future. The VSM prompt involved the participants viewing a brief video segment of themselves being asked the target question, stating personal information, followed by praise and reinforcement as the consequence. Additional procedures included the use of a cue (i.e., open hand followed by a snap), and a differential observing response (i.e., the participant was given an echoic cue of the differential words before the test) to increase correct responding. All three participants scored 0% correct independent responding during baseline followed by idiosyncratic results for targets taught through VSM and echoic prompting. At least two-thirds of mastered targets generalized across environments and therapists, and maintained for up to two weeks after treatment. Future research should conduct a component analysis of the video self-model to conclude key components promoting successful learning.
Recommended Citation
Carlos, Diana C., "Training Personal Information to Children Diagnosed with Autism Spectrum Disorder" (2017). Theses and Dissertations. 103.
https://repository.fit.edu/etd/103
Comments
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