https://github.com/nathan-lindstedt/student_risk

Student Success Model (SSM)
https://github.com/nathan-lindstedt/student_risk

fairlearn machine-learning scikit-learn shap student-risk xgboost

Last synced: 26 days ago
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Student Success Model (SSM)

• Host: GitHub
• URL: https://github.com/nathan-lindstedt/student_risk
• Owner: nathan-lindstedt
• License: mit
• Created: 2024-09-11T00:29:02.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-26T06:09:48.000Z (about 1 month ago)
• Last Synced: 2024-09-30T09:03:37.399Z (about 1 month ago)
• Topics: fairlearn, machine-learning, scikit-learn, shap, student-risk, xgboost
• Language: Python
• Homepage:
• Size: 145 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# student_risk

THE STRATEGIC USE OF CONTEMPORANEOUS DATA AND PREDICTIVE MODELS FOR A CONTEXT-DEPENDENT LIFE-CYCLE APPROACH TO STUDENT RETENTION

Abstract

The impending enrollment crunch, stemming from a reduced college-age population, compounds the need for higher educational institutions to focus more attention on efforts to retain students throughout their undergraduate careers. In this paper, we present a framework for the strategic use of contemporaneous data and predictive models to identify first-time and transfer students at risk of withdrawal in their initial two years at a four-year, land-grant university across a diverse, multi-campus system. When moving toward a more context-dependent life-cycle approach to student retention, considerations must be given for the unique context each campus creates for its students and for the salient factors affecting student outcomes at each stage of their undergraduate careers. To this end, we highlight the practical decisions that have been made along the way in establishing this framework, from those regarding how datasets are created for campuses over time to those regarding the selection of predictive models at different timepoints during students’ tenure.