Fuzzy Based Model for Students Debar Policy in Indian Engineering Institutes

Abstract

All around the world, a critical aspect of the higher education system is the evaluation of students through periodic examinations. To exemplify, many higher education centers in India allow students to undertake rigorous semester-based examinations i.e., End-Semester (or End-Term) examination, provided they meet the criteria of class attendance up to a certain percentage. However, below the mandatory percentage, the students are considered debarred from the examination. There are several instances been observed, especially since the Covid-19 cases arrived in India, where students have missed their classes due to genuinely unfavorable causes. In such cases, debarring students due to insufficient classroom attendance is unfair and this can affect students’ careers in adverse ways. To work in this direction, this paper analyses a computational model that takes into account multiple parameters reflecting students’ performance to determine whether they should be allowed to undertake the End-Term examination or not. The proposed model implements the machine learning-based K-means clustering and Fuzzy Modelling techniques, as an inclusive approach for strategic examination debars policy in engineering institutes. It is observed that in comparison to the other existing models, quite fewer students are declared as debarred using the proposed model. To the best of the authors' knowledge, no such system exists to date.



Author Information
Arti Jain, Jaypee Institute of Information Technology, India
Parmeet Kaur, Jaypee Institute of Information Technology, India
Shikha Jain, Jaypee Institute of Information Technology, India
Jorge Luis Morato Lara, Universidad Carlos III de Madrid, Spain

Paper Information
Conference: ECE2022
Stream: Higher education

This paper is part of the ECE2022 Conference Proceedings (View)
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Posted by amp21