Machine learning's model-agnostic interpretability on The Prediction of Students' Academic Performance in Video-Conference-Assisted Online Learning During the Covid-19 Pandemic (original) (raw)

Abstract

The Covid-19 pandemic had an immediate impact on higher education. Although online technology has made contributions to higher education, its adoption has had a significant impact on learning activities during the Covid-19 pandemic. This paper proposed a predictive model for predicting students’ academic performance in video-conference-assisted online learning (VCAOL) during Covid-19 pandemic based on machine learning approach. We investigated: Random Forest (RF), Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB). There were 361 data gathered as a dataset from September 2022 to January 2023. The overall result revealed RF outperformed SVM and GNB with accuracy score of 60.27%, precision 59.46%, recall 60.27%, F1-score 59.51% and ROC AUC 87%. Understanding a machine learning model's black-box output was crucial for providing predictions that explain why and how they were developed. SHAP value of global interpretability to visualize global feature importance revealed tha...

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