eprintid: 16858
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/68/58
datestamp: 2023-12-19 03:23:21
lastmod: 2023-12-19 03:23:21
status_changed: 2023-12-19 03:07:01
type: article
metadata_visibility: show
creators_name: Ahmad, M.
creators_name: Arshad, N.I.
creators_name: Sarlan, A.B.T.
title: USING DATA MINING TECHNIQUES TO MEASURE STUDENTS� ACADEMIC PERFORMANCE AND THE EFFECTIVENESS OF LEARNING METHODS: A LITERATURE REVIEW
ispublished: pub
note: cited By 0
abstract: 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.
date: 2022
publisher: Little Lion Scientific
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128638889&partnerID=40&md5=3bd7a7887f66f69aa85c4859d99413d2
full_text_status: none
publication: Journal of Theoretical and Applied Information Technology
volume: 100
number: 7
pagerange: 2300-2312
refereed: TRUE
issn: 19928645
citation:   Ahmad, M. and Arshad, N.I. and Sarlan, A.B.T.  (2022) USING DATA MINING TECHNIQUES TO MEASURE STUDENTS� ACADEMIC PERFORMANCE AND THE EFFECTIVENESS OF LEARNING METHODS: A LITERATURE REVIEW.  Journal of Theoretical and Applied Information Technology, 100 (7).  pp. 2300-2312.  ISSN 19928645