Machine Learning Based Student Grade Prediction: A Case Study

TL;DR

This paper explores machine learning techniques like CF, MF, and RBM to predict student grades, aiming to assist students and instructors by identifying those needing support based on real-world data from ITU Lahore.

Contribution

It systematically compares ML techniques for student grade prediction and finds RBM to be the most effective method among those tested.

Findings

RBM outperforms CF and MF in accuracy

Machine learning can effectively predict student performance

Potential to improve student support strategies

Abstract

In higher educational institutes, many students have to struggle hard to complete different courses since there is no dedicated support offered to students who need special attention in the registered courses. Machine learning techniques can be utilized for students' grades prediction in different courses. Such techniques would help students to improve their performance based on predicted grades and would enable instructors to identify such individuals who might need assistance in the courses. In this paper, we use Collaborative Filtering (CF), Matrix Factorization (MF), and Restricted Boltzmann Machines (RBM) techniques to systematically analyze a real-world data collected from Information Technology University (ITU), Lahore, Pakistan. We evaluate the academic performance of ITU students who got admission in the bachelor's degree program in ITU's Electrical Engineering department. The RBM technique is found to be better than the other techniques used in predicting the students' performance in the particular course.