Real‐Time Data and Predictive Analytics: Where Does IR Fit?

2022

J Intellect Disabil

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.

Section: Introductionmentioning

confidence: 99%

Post-high school outcomes of students with autism spectrum disorder and students with intellectual disability: Utilizing predictive analytics and state data for decision making

2022

J Intellect Disabil

“…In the most recent decade, another area has garnered increased attention, the post-school outcomes (PSO) of students with disabilities (SWD) (see Lipscomb et al, 2017). At the same time, newer technology has produced new tools, such as predictive modeling with machine learning (see Nassif et al, 2016), to analyze large administrative data, such as PSO, to support decision making (see Larkan-Skinner & Shedd, 2021). Utilizing these data and analytic tools is especially important for educators in closely examining the PSO of SWD (Mandinach, 2012), who continue to experience significant barriers to and challenges in successfully transitioning from high school to young-adult life (Liu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Outcomes of Students With Disabilities After Exiting From High School: A Study of Education Data Use and Predictive Analytics

2022

Journal of Education

We conducted a study of predictive analytics (PA) applied to state data on post-school outcomes (PSO) of exited high-school students with disabilities (SWD). Data analyses with machine learning Random Forest algorithm and multilevel Bayesian ordered logistic regression produced two key findings. One, Random Forest models were accurate in predicting PSO. Two, Bayesian models found high-school graduation was the strongest predictor of higher education and reliably predicted the specific type of outcome relative to other outcomes. Limitations of this study are the data source and small number of predictors. Implications of the study for researchers and educators are discussed in conclusion.

“…In the last decade there has been greater emphasis in a different research area, exited high-school students with disabilities (SWD) and their post-school outcomes (PSO) (see Lipscomb et al, 2017). Meanwhile, technological advances have also expanded the ways large administrative data, such as PSO, can be analyzed to support decision making (see Larkan-Skinner & Shedd, 2021) through predictive modeling approaches (e.g., Nassif et al, 2016). Using these data and analytic tools is necessary for state and local educators (Mandinach, 2012), especially the state-level PSO of those with autism spectrum disorder (ASD), who continue to face significant barriers in successfully transitioning from high school to post-high school life (Liu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

From high school to postsecondary education, training, and employment: Predicting outcomes for young adults with autism spectrum disorder

Autism & Developmental Language Impairments

2022

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.