A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

• Published in: International Journal of Modern Education and Computer Science
• Main Author: Tarmizi S.S.A.; Mutalib S.; Hamid N.H.A.; Rahman S.A.
• Format: Article
• Language: English
• Published: Modern Education and Computer Science Press 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130581452&doi=10.5815%2fijmecs.2019.08.01&partnerID=40&md5=0ad1937b8acf28740a62bbe70fb00a0e

Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students’ information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE. © 2019 MECS.