A Machine Learning system to monitor student progress in educational institutes

TL;DR

This paper presents a machine learning-based system that generates a credit score to monitor student progress, helping educators identify factors affecting performance and improve educational outcomes.

Contribution

It introduces a novel credit score classifier for student progress monitoring using machine learning, demonstrated through simulated data under simplified assumptions.

Findings

Credit score effectively indicates student progress.

System can identify activities linked to poor performance.

Potential integration with Learning Management Systems.

Abstract

In order to track and comprehend the academic achievement of students, both private and public educational institutions devote a significant amount of resources and labour. One of the difficult issues that institutes deal with on a regular basis is understanding the exam shortcomings of students. The performance of a student is influenced by a variety of factors, including attendance, attentiveness in class, understanding of concepts taught, the teachers ability to deliver the material effectively, timely completion of home assignments, and the concern of parents and teachers for guiding the student through the learning process. We propose a data driven approach that makes use of Machine Learning techniques to generate a classifier called credit score that helps to comprehend the learning journeys of students and identify activities that lead to subpar performances. This would make it easier for educators and institute management to create guidelines for system development to increase productivity. The proposal to use credit score as progress indicator is well suited to be used in a Learning Management System. In this article, we demonstrate the proof of the concept under simplified assumptions using simulated data.