A Collaborative Filtering Based Approach for Recommending Elective Courses

TL;DR

This paper develops a collaborative filtering-based recommendation system to help students select electives by predicting their potential grades, thereby aiding decision-making in management education.

Contribution

It extends collaborative filtering to predict student grades for elective courses, providing a novel approach for personalized course recommendations.

Findings

The system accurately predicts student grades for electives.

Experimental results show improved recommendation accuracy.

The approach is effective on real-world data.

Abstract

In management education programmes today, students face a difficult time in choosing electives as the number of electives available are many. As the range and diversity of different elective courses available for selection have increased, course recommendation systems that help students in making choices about courses have become more relevant. In this paper we extend the concept of collaborative filtering approach to develop a course recommendation system. The proposed approach provides student an accurate prediction of the grade they may get if they choose a particular course, which will be helpful when they decide on selecting elective courses, as grade is an important parameter for a student while deciding on an elective course. We experimentally evaluate the collaborative filtering approach on a real life data set and show that the proposed system is effective in terms of accuracy.