Citation

Teoh, Chin Wei and Ho, Sin Ban and Dollmat, Khairi Shazwan and Chai, Ian (2022) An Evolutionary Algorithm-Based Optimization Ensemble Learning Model for Predicting Academic Performance. In: ICSCA 2022: 2022 11th International Conference on Software and Computer Applications, 24 - 26 February 2022, Melaka Malaysia.

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Abstract

The significant growth of the Massive Open Online Course (MOCC) over last decade has promoted the rise of the educational data mining era in online education domain. This situation has created an opportunity for an educator to utilize the available data from MOOCs to facilitate student learning and performance. Therefore, this research study aims to introduce three types of ensemble learning methods, which are stacking, boosting, and bagging, to predict student performance on the online learning data that contain 480 students with 17 features. These techniques have to include the evolutionary algorithm to select the optimal number of input parameter to build the ensemble learning model. As a result, the proposed stacking type ensemble classifier has shown the highest prediction accuracy of approximately 88% and Area Under the Curve (AUC) of approximately 0.85. Results by stacking ensemble classifier have outperformed other ensemble classifiers, bagging and boosting as well as base classifiers.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Algorithms
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	29 Jul 2022 03:17
Last Modified:	29 Jul 2022 03:17
URII:	http://shdl.mmu.edu.my/id/eprint/10248

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