Who will dropout from university? Academic risk prediction based on interpretable machine learning
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2021
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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.
| Reference Key |
yang2021who
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|---|---|
| Authors | Shudong Yang |
| Journal | arXiv |
| Year | 2021 |
| DOI |
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