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AI-Driven Strategies for Reducing Student Withdrawal -- A Study of EMU Student Stopout

Yan Zhao, Amy Otteson

TL;DR

This study analyzes EMU stopout among first-time in any college (FTIAC) students to identify predictors of withdrawal and to enable early risk prediction. It combines Pearson correlation analyses with a predictive modeling approach using EMU data from 2013–2017, employing SMOTE to address class imbalance and XGBoost for retention prediction, achieving an overall accuracy of $77.7\%$. Key findings show strong positive associations between early academic performance (term GPA and first-fall earned SCH) and retention, and that admissions indicators (ACT scores and Decision GPA) also predict persistence, while demographics do not show significant effects. The resulting model supports timely, targeted interventions (e.g., tutoring, advising, financial aid counseling) to improve persistence, with potential applicability to EMU and similar institutions facing retention challenges.

Abstract

Not everyone who enrolls in college will leave with a certificate or degree, but the number of people who drop out or take a break is much higher than experts previously believed. In December 2013, there were 29 million people with some college education but no degree. That number jumped to 36 million by December of 2018, according to a new report from the National Student Clearinghouse Research Center[1]. It is imperative to understand the underlying factors contributing to student withdrawal and to assist decision-makers to identify effective strategies to prevent it. By analyzing the characteristics and educational pathways of the stopout student population, our aim is to provide actionable insights that can benefit institutions facing similar challenges. Eastern Michigan University (EMU) faces significant challenges in student retention, with approximately 55% of its undergraduate students not completing their degrees within six years. As an institution committed to student success, EMU conducted a comprehensive study of student withdrawals to understand the influencing factors. And the paper revealed a high correlation between certain factors and withdrawals, even in the early stages of university attendance. Based on these findings, we developed a predictive model that employs artificial intelligence techniques to assess the potential risk that students abandon their studies. These models enable universities to implement early intervention strategies, support at-risk students, and improve overall higher education success.

AI-Driven Strategies for Reducing Student Withdrawal -- A Study of EMU Student Stopout

TL;DR

This study analyzes EMU stopout among first-time in any college (FTIAC) students to identify predictors of withdrawal and to enable early risk prediction. It combines Pearson correlation analyses with a predictive modeling approach using EMU data from 2013–2017, employing SMOTE to address class imbalance and XGBoost for retention prediction, achieving an overall accuracy of . Key findings show strong positive associations between early academic performance (term GPA and first-fall earned SCH) and retention, and that admissions indicators (ACT scores and Decision GPA) also predict persistence, while demographics do not show significant effects. The resulting model supports timely, targeted interventions (e.g., tutoring, advising, financial aid counseling) to improve persistence, with potential applicability to EMU and similar institutions facing retention challenges.

Abstract

Not everyone who enrolls in college will leave with a certificate or degree, but the number of people who drop out or take a break is much higher than experts previously believed. In December 2013, there were 29 million people with some college education but no degree. That number jumped to 36 million by December of 2018, according to a new report from the National Student Clearinghouse Research Center[1]. It is imperative to understand the underlying factors contributing to student withdrawal and to assist decision-makers to identify effective strategies to prevent it. By analyzing the characteristics and educational pathways of the stopout student population, our aim is to provide actionable insights that can benefit institutions facing similar challenges. Eastern Michigan University (EMU) faces significant challenges in student retention, with approximately 55% of its undergraduate students not completing their degrees within six years. As an institution committed to student success, EMU conducted a comprehensive study of student withdrawals to understand the influencing factors. And the paper revealed a high correlation between certain factors and withdrawals, even in the early stages of university attendance. Based on these findings, we developed a predictive model that employs artificial intelligence techniques to assess the potential risk that students abandon their studies. These models enable universities to implement early intervention strategies, support at-risk students, and improve overall higher education success.
Paper Structure (26 sections, 1 equation, 5 figures, 2 tables)

This paper contains 26 sections, 1 equation, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Collection
  4. Correlaiton Experiments
  5. Pearson Correlation Coefficient Calculation
  6. Academic Standing (GPA and SCH)
  7. Term GPA and Retention Rate Data
  8. Correlation Results
  9. Key Insights
  10. Admission Criteria (Test Scores and Decision GPA)
  11. Demographic Information
  12. Finacial Status
  13. Predictive Model
  14. Model Development
  15. Data Preparation
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Retention Rates over Six Years by Term GPA
  • Figure 2: Retention Rates over Six Years by SCH
  • Figure 3: Retention Rates over Six Years of ACT Score Group
  • Figure 4: Retention Rates over Six Years by Decision GPA
  • Figure 5: Retention Rates over Six Years of Age Group