Predicting At-Risk Programming Students in Small Imbalanced Datasets using Synthetic Data

TL;DR

This paper demonstrates that synthetic data generation significantly enhances machine learning models' ability to identify at-risk programming students in small, imbalanced datasets, enabling earlier interventions.

Contribution

It introduces a novel approach combining synthetic data with machine learning to improve early detection of at-risk students in programming courses with limited data.

Findings

Synthetic data improves recall for failing students

Machine learning models can predict at-risk students earlier

Feature importance analysis aids in refining educational interventions

Abstract

This study is part of a larger project focused on measuring, understanding, and improving student engagement in programming education. We investigate whether synthetic data generation can help identify at-risk students earlier in a small, imbalanced dataset from an introductory programming module. The analysis used anonymised records from 379 students, with 15\% marked as failing, and applied several machine learning algorithms. The first experiments showed poor recall for the failing group. However, using synthetic data generation methods led to a significant improvement in performance. Our results suggest that machine learning can help identify at-risk students early in programming courses when combined with synthetic data. This research lays the groundwork for validating and using these models with live student cohorts in the future, to allow for timely and effective interventions that can improve student outcomes. It also includes feature importance analysis to refine formative tasks. Overall, this study contributes to developing practical workflows that help detect disengagement early and improve student success in programming education.