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

Abstract

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.

Automation Process for Learning Outcome Predictions

“…The model's prowess stems from its ability to handle categorical data, a common characteristic of educational datasets. Logistic Regression shines in its simplicity and interpretability, a crucial aspect when educators and policymakers are at the helm, making decisions based on its predictions [7].…”