# A Machine Learning system to monitor student progress in educational institutes

**Authors:** Bibhuprasad Mahakud, Bibhuti Parida, Ipsit Panda, Souvik Maity, Arpita Sahoo, Reeta Sharma  
**arXiv:** [2211.05829](https://arxiv.org/abs/2211.05829)  
**Date:** 2022-11-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2211.05829v1/

## Key points

- Academic achievement tracking is a significant investment for educational institutions
- Understanding reasons behind students' exam shortcomings is a recurring challenge
- Factors influencing student performance include attendance, attentiveness, understanding of concepts, teacher delivery, assignment completion, and guidance from parents and teachers
- The classifier aims to provide insights into students' learning journeys and identify activities contributing to subpar performances
- Implementation of the system can establish guidelines for enhancing productivity
- Credit scores as progress indicators are suitable for integration into a Learning Management System
- The study presents a proof of concept using simulated data under simplified assumptions
- Machine Learning can effectively monitor student progress
- Authors: Bibhuprasad Mahakud, Bibhuti Parida, Ipsit Panda, Souvik Maity, Arpita Sahoo, Reeta Sharma

## Summary

In order to track and comprehend the academic achievement of students, both private and public educational institutions invest significant resources and labor. One of the recurring challenges faced by these institutes is understanding the reasons behind students' exam shortcomings. Various factors influence a student's performance, including attendance, attentiveness in class, understanding of taught concepts, effective delivery of material by teachers, timely completion of assignments, and the guidance provided by parents and teachers throughout the learning process. This classifier aims to provide insights into students' learning journeys and identify activities that contribute to subpar performances. By implementing this system, educators and institute management can establish guidelines for system development to enhance productivity. The concept of using credit scores as progress indicators is particularly suitable for integration into a Learning Management System. In this article, we present a proof of concept using simulated data under simplified assumptions. The study demonstrates how Machine Learning can be leveraged to monitor student progress effectively. The authors of this research are Bibhuprasad Mahakud, Bibhuti Parida, Ipsit Panda, Souvik Maity, Arpita Sahoo, and Reeta Sharma. The abstract provides an overview of the challenges faced by educational institutions in tracking student performance and introduces the proposed data-driven approach using Machine Learning techniques. It highlights potential benefits such as improved educational outcomes through leveraging technology and data analysis methods with a credit score-based monitoring system in educational settings. Overall, this study contributes valuable insights into improving educational outcomes by leveraging technology and data analysis methods.