From High School to Postsecondary Education, Training, and Employment: Predicting Outcomes for Young Adults with Autism Spectrum Disorder

Autor/inn/en	Yamamoto, Scott H.; Alverson, Charlotte Y.
Titel	From High School to Postsecondary Education, Training, and Employment: Predicting Outcomes for Young Adults with Autism Spectrum Disorder
Quelle	In: Autism & Developmental Language Impairments, 7 (2022), (12 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Yamamoto, Scott H.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1177/23969415221095019
Schlagwörter	Autism Spectrum Disorders; Students with Disabilities; High School Graduates; Outcomes of Education; Postsecondary Education; Prediction; Predictive Measurement; Data Analysis; Artificial Intelligence; Predictor Variables; Regression (Statistics); Accuracy; Reliability + Suchen Sie Ihr Suchwort? Autism; Autismus; Student; Students; Disability; Disabilities; Schüler; Schülerin; Studentin; Behinderung; High school; High schools; Graduate; Graduates; Oberschule; Absolvent; Absolventin; Lernleistung; Schulerfolg; Post-secondary education; Tertiäre Bildung; Vorhersage; Auswertung; Künstliche Intelligenz; Prädiktor; Regression; Regressionsanalyse; Reliabilität
Abstract	Background and Aims: The fastest growing group of students with disabilities are those with Autism Spectrum Disorder (ASD). States annually report on post-high school outcomes (PSO) of exited students. This study sought to fill two gaps in the literature related to PSO for exited high-school students with ASD and the use of state data and predictive modeling. Methods: Data from two states were analyzed using two predictive analytics (PA) methods: multilevel logistic regression and machine learning. The receiver operating characteristic curve (ROC) analysis was used to assess predictive performance. Results: Data analyses produced two results. One, the strongest predictor of PSO for exited students with ASD was graduating from high school. Two, machine learning performed better than multilevel logistic regression in predicting PSO engagement across the two states. Conclusion: This study contributed two new and important findings to the literature: (a) PA models should be applied to state PSO data because they produce useful information, and (b) PA models are accurate and reliable over time. Implications: These findings can be used to support state and local educators to make decisions about policies, programs, and practices for exited high school students with ASD, to help them successfully transition to adult life. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01