TY - JOUR
T1 - Application of educational data mining approach for student academic performance prediction using progressive temporal data
AU - Trakunphutthirak, Ruangsak
AU - Lee, Vincent C.S.
N1 - Publisher Copyright:
© The Author(s) 2021.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2022
Y1 - 2022
N2 - Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy.
AB - Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy.
KW - at-risk students
KW - educational data mining
KW - log files
KW - machine learning techniques
UR - http://www.scopus.com/inward/record.url?scp=85116044586&partnerID=8YFLogxK
U2 - 10.1177/07356331211048777
DO - 10.1177/07356331211048777
M3 - Article
AN - SCOPUS:85116044586
SN - 0735-6331
VL - 60
SP - 742
EP - 776
JO - Journal of Educational Computing Research
JF - Journal of Educational Computing Research
IS - 3
ER -