Abstract: Dropout remains a persistent challenge within high school education. In this paper, we present a case study on automatically detecting whether a student is at-risk of dropout within a diverse school district in Texas. We predict whether a student will drop out in a future school year from data on students' discipline, attendance, course-taking, and grades, using a logistic regression framework. We discuss the predictive properties of the model, and the features that are predictive of dropout in this context.
Publication Year: 2019
Publication Date: 2019-10-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 25
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot