Title: Student absenteeism in engineering college using rough set and data mining approach

Authors: I. Samuel Peter James; P. Ramasubramanian; D. Magdalene Delighta Angeline

Addresses: Department of Computer Science, Bharathiar University, Coimbatore – 641046, Tamilnadu, India ' Department of Computer Science and Engineering, Shadan Women's College of Engineering and Technology, Hyderabad – 500004, India ' Department of Computer Science and Engineering, Joginpally B.R. Engineering College, Hyderabad – 500075, India

Abstract: Now-a-days student absenteeism in engineering education is a most important issue of a professional institution which affects the overall performance of institutions. This is most imperative alarm in creating outstanding engineers (real engineers) to the country. The quality of education is directly proportional to student absenteeism. A technique used for analysing the attributes that influence the subject on the total scores of the students was rough set theory (RST). In this paper, the author investigates, why students are absent in most of the engineering institutions and to work out the decision-making methods for grade options that manage student's absenteeism by using RST. In this study, the faculty qualifications, experience, communication skills, subject knowledge, number of guest lecturer/workshop/seminars conducted, college infrastructure, counselling, special coaching for weak students, subject understanding concepts and so forth, collected from students are considered.

Keywords: college-based behavioural issues; data mining; education; EDM; educational data mining; engineering college; quality education; quality teaching; rough set; student absenteeism; technical institution.

DOI: 10.1504/IJAIP.2022.126700

International Journal of Advanced Intelligence Paradigms, 2022 Vol.23 No.3/4, pp.423 - 433

Accepted: 19 Dec 2019
Published online: 03 Nov 2022 *

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