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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Author 1: Phauk Sokkhey
Author 2: Takeo Okazaki

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110704

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.

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Abstract: The primary goal of educational systems is to enrich the quality of education by maximizing the best results and minimizing the failure rate of poor-performing students. Early predicting student performance has become a challenging task for the improvement and development of academic performance. Educational data mining is an effective discipline of data mining concerned with information integrated into the education domain. The study is of this work is to propose techniques in educational data mining and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models was conducted. Subsequently, high performing models were developed to get higher performance. The hybrid random forest named Hybrid RF produces the most successful classification. For the context of intervention and improving the learning outcomes, a novel feature selection method named MICHI, which is the combination of mutual information and chi-square algorithms based on the ranked feature scores is introduced to select a dominant set and improve performance of prediction models. By using the proposed techniques of educational data mining, and academic performance prediction system is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental results and evaluation surveys report the effectiveness and usefulness of the developed academic prediction system. The system is used to help educational stakeholders for intervening and improving student performance.

Keywords: Academic performance prediction systems; educational data mining; dominant factors; feature selection methods; prediction models; student performance

Phauk Sokkhey and Takeo Okazaki, “Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110704

@article{Sokkhey2020,
title = {Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110704},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110704},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Phauk Sokkhey and Takeo Okazaki}
}


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