Quality enhancement at higher education institutions by early identifying students at risk using data mining

  • Khalid Mahboob Department of Computer Science and Information Technology, N.E.D University of Engineering and Technology, Karachi Pakistan
  • Raheela Asif Department of Software Engineering, N.E.D University of Engineering and Technology, Karachi Pakistan
  • Najmi Ghani Haider Department of Computing Sciences, U.I.T University, Karachi Pakistan

Abstract

Accurate prediction of students' academic performance is one of the challenges in maintaining quality standards in any Higher Education Institution (H.E.I.). To ensure the quality of teaching and learning, H.E.I.s often employ Self-Assessment Reports (S.A.R.s) in which identifying a student drop-out ratio is important. Hence, it is essential to identify at-risk students in a given academic program. This article aims to identify at-risk students early by proposing a data mining-based predictive framework to improve the student's learning experience and minimize the dropped-out ratio. The academic sub-attributes or indicators in each course that may affect the performance of students in higher education institutions used in this study to examine students' academic achievement and predict students' performance to distinguish at-risk students are the marks of assignments, mid-term, lab exams, semester marks, total, grade, grade point (G.P.), quality point (Q.P.), grade point average (G.P.A.), and credit hours data of multiple courses categorized according to three knowledge areas defined by Higher Education Commission (H.E.C), Pakistan using data mining predictive techniques. The results indicate that the proposed methods can achieve maximum accuracy in predicting and identifying at-risk students in different courses.

Published
Jan 1, 2023
How to Cite
MAHBOOB, Khalid; ASIF, Raheela; HAIDER, Najmi Ghani. Quality enhancement at higher education institutions by early identifying students at risk using data mining. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 42, n. 1, p. 120-136, jan. 2023. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2451>. Date accessed: 06 feb. 2023. doi: http://dx.doi.org/10.22581/muet1982.2301.12.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License