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.

中文翻译：

---

从高中到高等教育、培训和就业：预测患有自闭症谱系障碍的年轻人的结果

背景和目标

增长最快的残疾学生群体是患有自闭症谱系障碍 (ASD) 的学生。各州每年都会报告退学学生的高中毕业后成绩 (PSO)。本研究旨在填补与 ASD 退学高中生的 PSO 相关文献中的两个空白以及状态数据和预测模型的使用。

方法

来自两个州的数据使用两种预测分析 (PA) 方法进行分析：多级逻辑回归和机器学习。接受者操作特征曲线 (ROC) 分析用于评估预测性能。

结果

数据分析产生了两个结果。第一，对于退出的 ASD 学生，PSO 的最强预测因素是高中毕业。第二，机器学习在预测两个州的 PSO 参与度方面比多级逻辑回归表现更好。

结论

这项研究为文献贡献了两个新的重要发现：(a) PA 模型应该应用于状态 PSO 数据，因为它们会产生有用的信息，以及 (b) PA 模型随着时间的推移是准确和可靠的。

启示

这些发现可用于支持州和地方教育工作者为患有 ASD 的退学高中生制定政策、计划和实践决策，以帮助他们成功过渡到成人生活。