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Autor/inn/enRadwan, Akram M.; Birkan, Binyamin; Hania, Fadi; Cataltepe, Zehra
TitelActive Machine Learning Framework for Teaching Object Recognition Skills to Children with Autism
QuelleIn: International Journal of Developmental Disabilities, 63 (2017) 3, S.158-169 (12 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN2047-3869
DOI10.1080/20473869.2016.1190543
SchlagwörterComputer Uses in Education; Assistive Technology; Electronic Learning; Teaching Methods; Recognition (Psychology); Active Learning; Students with Disabilities; Special Schools; Foreign Countries; Outcomes of Education; Autism; Pervasive Developmental Disorders; Gaza Strip; Palestine
AbstractActive machine learning (AML) techniques enable a machine learning model to perform better with less labeled training data. In this study, we proposed an AML approach for teaching object recognition skills to children with autism spectrum disorders (ASD) and compared its effects with passive learning (PL). A web and touch-based application was developed for teaching object recognition where objects were grouped according to their categories and difficulty levels. The teaching procedure was based on Applied Behavioral Analysis principles. Five children with mild to moderate levels of ASD participated in the study. An alternating treatments design of single-subject research methods was used. The results indicated that AML was more effective than PL for four out of the five participants. Consequently, they can learn faster with fewer teaching trials that are required to reach a learning criterion. (As Provided).
AnmerkungenRoutledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2020/1/01
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