Courtesy Faculty, University of Oregon College of Education, Eugene, OR, USA.
Research Associate Professor, Secondary Special Education and Transition Program, University of Oregon College of Education, Eugene, OR, USA.
J Intellect Disabil. 2023 Sep;27(3):633-647. doi: 10.1177/17446295221100039. Epub 2022 May 9.
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.
本研究分析了美国西南部一个州已离校的智障和自闭症谱系障碍高中生的高中后去向。采用预测分析方法分析了这些学生的高中后去向数据，这是根据美国特殊教育法，每个州每年都必须收集的数据。数据建模采用机器学习和逻辑回归，得出了两个主要发现。其一，最重要的显著预测因素是：(a) 学生至少有 80%的教学日在普通教育环境中度过，(b) 高中毕业。其二，机器学习模型在预测高中后教育或就业方面始终比多层次逻辑回归模型更准确。本研究最后讨论了数据和预测分析模型的局限性，以及为研究人员以及州和地方教育专业人员利用预测分析和州级高中后去向数据进行决策提供了启示。
