Chi-Square Oversampling to Improve Dropout Prediction Performance in Massive Open Online Courses

Liliana, Liliana and Santosa, Paulus Insap and Hartanto, Rudy and Kusumawardani, Sri Suning (2025) Chi-Square Oversampling to Improve Dropout Prediction Performance in Massive Open Online Courses. International Journal of Technology (IJTech), 16 (4). pp. 1220-1231. ISSN 2086-9614

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Abstract

Massive Open Online Courses (MOOCs) are important to achieve educational quality in Indonesia. However, low retention rates are global problems that must be addressed by building a prediction model to prevent dropout. The prediction model faces a challenge due to the disproportionate comparison between major and minor data. In this study, the 141 datasets collected from the questionnaire consisted of 95% participant data who completed the course and 5% dropout data. This necessitated oversampling to balance the data using Synthetic Minority Over-sampling Technique for Nominal (SMOTE-N) and SMOTE for Encoded Nominal and Continuous (SMOTE-ENC) chi-square methods. The dataset formed was processed using Support Vector Machine (SVM) machine learning method. In the testing process, the performance of the prediction model with SMOTE-N and SMOTE-ENC chi-square oversampling data was compared with the prediction model with regular oversampling data. The results showed a significant increase in accuracy from each oversampling method with weighting. SMOTE-N weighting modification using chi-square value had the best value, with F1-measure reaching 95.33%, and a decrease in error in the prediction of dropout data was observed. This result showed that the model formed with the SMOTE-N chi-square method has good predictive ability.

Item Type:	Article
Uncontrolled Keywords:	Chi-square; Dropout prediction; Indonesia; MOOCs; Oversampling
Subjects:	H Social Sciences > H Social Sciences (General) L Education > L Education (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Information System
Depositing User:	LILIANA
Date Deposited:	18 Jul 2025 03:47
Last Modified:	18 Jul 2025 03:47
URI:	http://repository.ubaya.ac.id/id/eprint/49021

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