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Predictive modeling and cohort data analytics for student success and retention
1Computer Engineering and Computer Science Department, California State University, Long Beach, 90840, CA, USA.
Evaluation and Program Planning
|September 2, 2025
Summary
Academic success varies by student demographics. Minority and Pell-eligible students face challenges, but predictive models can identify at-risk students for targeted support and improved retention.
Area of Science:
- Higher Education Research
- Educational Data Mining
- Student Success Analytics
Background:
- Academic performance and retention are critical metrics in higher education.
- Understanding demographic disparities is essential for equitable student support.
- Predictive modeling offers opportunities for early intervention in student success.
Purpose of the Study:
- To analyze academic performance disparities among diverse student populations.
- To identify key factors influencing student outcomes (GPA, credit accumulation).
- To develop and validate predictive models for forecasting student success.
Main Methods:
- Data-driven analysis of over 23,000 first-time freshmen.
- Examination of factors: GPA, credit accumulation, Pell Grant eligibility, minority status, parent education.
- Application of clustering analysis and deep learning for predictive modeling.
Main Results:
- Significant disparities observed: minority and Pell-eligible students accumulate fewer credits.
- Lower average GPA and broader GPA variation noted for minority students.
- Three distinct academic engagement profiles identified through clustering analysis.
Conclusions:
- Heterogeneous student performance necessitates differentiated support strategies.
- Predictive models accurately forecast sophomore credit accumulation and GPA.
- Findings provide actionable insights for enhancing student retention and academic momentum.
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