TY  - JOUR
VL  - 127
KW  - E-learning; K-means clustering; Learning systems; Students
KW  -  Academic performance; Clusterings; Covid-19; Data-mining techniques; Educational data mining; High educations; K-means; K-means clustering algorithms; Malaysia; Online learning
KW  -  Data mining
PB  - Springer Science and Business Media Deutschland GmbH
A1  - Ahmad, M.
A1  - Arshad, N.I.B.
A1  - Sarlan, A.B.
SP  - 309
AV  - none
Y1  - 2022///
N2  - A massive amount of data is often used to evaluate the academic performance of students in higher education. Analysis can solve this challenge through various strategies and methods. Due to the spread of the pandemic Covid-19, traditional modes of education have shifted to include online learning. This study aims to analyze the academic performance of students through data mining techniques. The objective aims to investigate the academic performance of business students at a private university in Malaysia using Educational Data Mining techniques. Studentsâ?? academic performance data of a private university in Malaysia is used to analyze studentsâ?? performance using demographic and academic attributes. This study used studentsâ?? academic performance in the learning method to identify the patterns before and during Covid-19 using the K-Means data mining clustering technique. The results of the k-means clustering analysis showed that students were achieving higher CGPA during Covid-19 online learning compared to before Covid-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ID  - scholars17695
EP  - 318
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127887124&doi=10.1007%2f978-3-030-98741-1_26&partnerID=40&md5=2d990d26b08daddb1b1c45680d3d452a
SN  - 23674512
JF  - Lecture Notes on Data Engineering and Communications Technologies
N1  - cited By 0
TI  - An Analysis of Studentsâ?? Academic Performance Using K-Means Clustering Algorithm
ER  -