Post-High School Outcomes of Students with Autism Spectrum Disorder and Students with Intellectual Disability: Utilizing Predictive Analytics and State Data for Decision Making

Autor/inn/en	Yamamoto, Scott H.; Alverson, Charlotte Y.
Titel	Post-High School Outcomes of Students with Autism Spectrum Disorder and Students with Intellectual Disability: Utilizing Predictive Analytics and State Data for Decision Making
Quelle	In: Journal of Intellectual Disabilities, 27 (2023) 3, S.633-647 (15 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Yamamoto, Scott H.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1744-6295
DOI	10.1177/17446295221100039
Schlagwörter	Students with Disabilities; Autism Spectrum Disorders; Intellectual Disability; Predictor Variables; Outcomes of Education; High School Students; Inclusion; High School Graduates; Graduation; Artificial Intelligence; Student Characteristics; Postsecondary Education; Employment Level + Suchen Sie Ihr Suchwort? Student; Students; Disability; Disabilities; Schüler; Schülerin; Studentin; Behinderung; Autism; Autismus; Intellect; Verstand; Prädiktor; Lernleistung; Schulerfolg; High school; High schools; Oberschule; Inklusion; Graduate; Graduates; Absolvent; Absolventin; Abschluss; Graduierung; Künstliche Intelligenz; Post-secondary education; Tertiäre Bildung; Beschäftigungsgrad
Abstract	This study analyzed the post-high school outcomes of exited high-school students with intellectual disability and autism spectrum disorder from a southwestern U.S. state. A predictive analytics approach was used to analyze these students' post-high school outcomes data, which every state is required to collect each year under U.S. special-education law. Data modeling was conducted with machine learning and logistic regression, which produced two main findings. One, the strongest significant predictors were (a) students spending at least 80% of their instructional days in general education settings and (b) graduating from high school. Two, machine learning models were consistently more accurate in predicting post-high school education or employment than were multilevel logistic regression models. This study concluded with the limitations of the data and predictive-analytic models, and the implications for researchers and state and local education professionals to utilize predictive analytics and state-level post-high school outcomes data for decision making. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01