Abstract
Students’ performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks’ students need to be observed and earlier preventive measures are required to be carried out so as to determine the cause of students’ dropout rate. Hence, the purpose of this research is to identify factors influencing students’ performance using educational data mining techniques. In order to achieve this, data from different sources is employed into a single platform for pre-processing and modelling. The design of the study is divided into 6 different phases (data collection, data integration, data pre-processing such as cleaning, normalization, and transformation, feature selection, patterns extraction and model optimization as well as evaluation. The datasets were collected from a students’ information system and e-learning system from a public university in Malaysia, while sample data from the Faculty of Engineering were used accordingly. This study also employed the use of academic, demographical, economical and behaviour e-learning features, in which 8 different group models were developed using 3 base-classifiers; Decision Tree, Artificial Neural Network and Support Vector Machine, and 5 multi-classifiers; Random Forest, Bagging, AdaBoost, Stacking and Majority Vote classifier. Finally, the highest accuracy of the classifier model was optimized. At the end, new Students’ Performance Prediction Model was developed. The result proves that combination demographics with behaviour using a meta-classifier model with optimized hyper parameter produced better accuracy to predict students’ performance.
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Acknowledgements
The authors are grateful to Research Management Centre (RMC), Universiti Teknologi Malaysia (UTM) for the financial support under Tier 2 Research University Grant (Q.K130000.2638.14J88).
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Hassan, H., Anuar, S., Ahmad, N.B. (2019). Students’ Performance Prediction Model Using Meta-classifier Approach. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_19
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