Who will dropout from university? Academic risk prediction based on interpretable machine learning

TL;DR

This paper develops an interpretable machine learning approach using LightGBM and Shapley values to predict university dropout risk based on student behavior data, highlighting key predictors and enabling personalized analysis.

Contribution

It introduces the first learning interaction networks for social behavior in academic risk prediction and applies Shapley values for transparent, personalized interpretability.

Findings

Key predictors include academic partner quality, classroom seating, and dorm atmosphere.

Characteristics like campus living and smoking show little correlation with dropout risk.

Personalized analysis reveals individual factors affecting academic risk.

Abstract

In the institutional research mode, in order to explore which characteristics are the best indicators for predicting academic risk from the student behavior data sets that have high-dimensional, unbalanced classified small sample, it transforms the academic risk prediction of college students into a binary classification task. It predicts academic risk based on the LightGBM model and the interpretable machine learning method of Shapley value. The simulation results show that from the global perspective of the prediction model, characteristics such as the quality of academic partners, the seating position in classroom, the dormitory study atmosphere, the English scores of the college entrance examination, the quantity of academic partners, the addiction level of video games, the mobility of academic partners, and the degree of truancy are the best 8 predictors for academic risk. It is contrary to intuition that characteristics such as living in campus or not, work-study, lipstick addiction, student leader or not, lover amount, and smoking have little correlation with university academic risk in this experiment. From the local perspective of the sample, the factors affecting academic risk vary from person to person. It can perform personalized interpretable analysis through Shapley values, which cannot be done by traditional mathematical statistical prediction models. The academic contributions of this research are mainly in two aspects: First, the learning interaction networks is proposed for the first time, so that social behavior can be used to compensate for the one-sided individual behavior and improve the performance of academic risk prediction. Second, the introduction of Shapley value calculation makes machine learning that lacks a clear reasoning process visualized, and provides intuitive decision support for education managers.