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• W4229376002 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." @default.
• W4229376002 created "2022-05-10" @default.
• W4229376002 creator A5048376135 @default.
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• W4229376002 date "2022-05-09" @default.
• W4229376002 modified "2023-09-24" @default.
• W4229376002 title "Post-high school outcomes of students with autism spectrum disorder and students with intellectual disability: Utilizing predictive analytics and state data for decision making" @default.
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• W4229376002 doi "https://doi.org/10.1177/17446295221100039" @default.
• W4229376002 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35533266" @default.
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