USING DATA MINING TECHNIQUES TO MEASURE STUDENTS� ACADEMIC PERFORMANCE AND THE EFFECTIVENESS OF LEARNING METHODS: A LITERATURE REVIEW

• Main Authors: Ahmad, M., Arshad, N.I., Sarlan, A.B.T.
• Format: Article
• Published: Little Lion Scientific 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128638889&partnerID=40&md5=3bd7a7887f66f69aa85c4859d99413d2; http://eprints.utp.edu.my/33138/

Analyzing students' academic performance in online learning to improve the overall quality and effectiveness of education has been one of the main focuses of Higher Educational Institutions (HEIs). A practical analysis utilizing the academic performance data to improve the quality of online learning has become a vital issue urgently required to guide HEIs for the improvement of the academic performance of students. The changes affected in the Covid-19 framework have affected the academic performance of students and educators. This study aims to summarize the various aspects of educational data mining and how it can be utilized to improve the teaching process. Using EDM, the study analyzed students' academic performance for the past five years. It focused on the various learning methods that the students used. The study provided a detailed analysis of the multiple attributes that influenced the students' academic performance. We presented 12 out of 56 papers/documents that fit the inclusion and exclusion criteria of students' academic performance based on the educational setting. This study revealed that the most commonly used methods for assessing students' academic performance are not done in the face-to-face learning method. © 2022 Little Lion Scientific.