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Asian Journal of Information and Communications 12(1)
Asian Journal of Information and Communications 12(1)

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Asian Journal of Information and Communications

Machine learning applications for learning early warning system in Taiwan Jyh-Jiuan Lin et al.

  • Page:0
  • 2020-10-07
This research proposes an alternative approach reference for a learning early warning
system implementation. Digital e-portfolio data of 6 semesters are used respectively to
build 4 commonly used supervised machine learning (ML) classifiers including random
forests (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and
artificial neural networks (ANN). The empirical results from year 2013 to 2019 semesters,
excluding 2018 due to sabbatical leave of the instructor, show that the top 2 classifiers are
XGBoost and RF in terms of the following aggregated criteria consideration: 1. Accuracy,
2. Recall, 3. Precision, 4. F1-score, 5. AUC, 6. Cross-validation mean accuracy, 7. Crossvalidation
accuracy standard deviation (StDev), and 8. Computation time. Since XGBoost
has outperformed the rest classifiers, it is recommended to be deployed by the early warning
system implementation. The evidence of the model robustness supports the approach of the
learning early warning systems implementation incorporating ML methods. Besides,
midterm score reaches a consensus for XGBoost and RF to be selected as the most
significant features to identify at-risk students. Interestingly, the second most significant
feature selected by RF is the “mock exam score”. It fits the purpose of mock exam which
is designed to help students foresee the midterm test format. On the other hand, the second
most significant feature selected by XGBoost is the “forum post counts” which implies that
the higher the participation is, the better the academic performance gets empirically.

Keywords: e-learning, e-portfolio, recommender system, learning management systems
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