Predicting Dropout Analysis

Predicting Dropout Before New Semester

Analyzing student retention patterns and predicting dropouts to improve retention rates before the new semester begins.

Executive Summary

This case study analyzes a student retention model designed to predict student enrollment and identify factors leading to potential dropouts. The key focus was to evaluate the model's accuracy and predict which students are at risk of not re-enrolling. The results showed that the majority of students are likely to stay enrolled, but a small portion was flagged as high-risk. Strategic interventions targeting this group can significantly improve retention rates and prevent future dropouts.

Problem Statement

Educational institutions face challenges in identifying students at risk of dropping out and understanding the factors contributing to these decisions. By analyzing student data—such as attendance, GPA, extracurricular participation, and financial aid status—the aim is to predict enrollment patterns for the upcoming semester and improve overall retention rates.

Approach

The data analysis utilized a student dataset to evaluate key factors influencing enrollment and dropout rates. The predictive model was built using the following steps:

  • Descriptive Analysis: Initial query reports analyzed current enrollment statistics and dropout factors.
  • Prediction Model: A machine learning model was applied to forecast retention, using key predictors such as GPA, attendance, and financial aid.
  • Model Evaluation: The performance of the model was measured using metrics such as precision, recall, F1 score, accuracy, and ROC AUC.

Results

Predicted Enrollment Patterns

  • Dropout Rate: The model predicts a dropout rate of 1.77% (53 students out of 3,000).
  • Retention Rate: 98.23% of students are predicted to stay enrolled, indicating a strong retention rate.
Predicted Enrollment Patterns

Key Risk Factors

  • At-Risk Group: Students with low attendance, low GPA, no extracurricular activities, and lack of financial aid are more likely to drop out.
  • Impact of Financial Aid: The absence of financial aid is a significant factor for students at risk of dropping out, showing that financial constraints strongly influence retention.
Key Risk Factors

Visualization

Explore the complete interactive visualization here:

Model Performance

  • Precision: 49.77% of predicted retained students were actually retained, highlighting moderate accuracy.
  • Recall: The model successfully identified 69.16% of students who remained enrolled.
  • F1 Score: The F1 score is 57.88%, reflecting a balance between precision and recall.
  • Accuracy: The model’s accuracy is 48.25%, suggesting room for improvement.
  • ROC AUC: The ROC AUC of 49.72% indicates the model's limited ability to distinguish between students who will drop out and those who will remain.
Model Performance

Recommendations

  • Targeted Interventions: Focus retention efforts on the 53 students identified as high-risk. Provide them with academic support, counseling, and financial aid to mitigate their dropout risk.
  • Expand Financial Aid: Increasing financial aid opportunities could improve retention, especially for students identified as financially vulnerable.
  • Monitor Risk Factors: Regularly monitor GPA, attendance, and extracurricular participation to proactively identify students at risk of disengagement.

Conclusion

The predictive model provided valuable insights into student retention patterns, particularly highlighting the importance of GPA, attendance, and financial aid in influencing student decisions to remain enrolled. Institutions can apply these findings to better allocate resources and support at-risk students, improving overall retention rates. By targeting interventions and expanding financial aid, educational institutions can reduce dropout rates and increase student success in the upcoming semester.