A machine learning framework to identify students at risk of adverse academic outcomes.


Many school districts have developed successful intervention programs to help students graduate high school on time. However, identifying and prioritizing students who need those interventions the most remains challenging. This paper describes a machine learning framework to identify such students, describes features that are useful for this task, applies several classification algorithms, and evaluates them using metrics important to school administrators. To help test this framework and make it practically useful, we partnered with two U.S. school districts with a combined enrollment of approximately of 200,000 students. We together designed metrics to evaluate the framework’s performance and tools such as interactive dashboards to help match at risk students with appropriate supports. This paper focuses on students at risk of not finishing high school on time, but our framework lays a foundation for future work on other adverse academic outcomes.

This publication has been peer reviewed.
Publication Type: 
Conference Proceedings
Lakkarajau, H.
Aguiar, E.
Shan, C.
Miller, D.
Bhanpuri, N.
Ghani, R.
Addison, K.
Year of Publication: 
Journal, Book, Magazine or Other Publication Title: 
21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publication Language: 
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