Optimal Group Formulation Using Machine Learning

TL;DR

This paper presents a machine learning-based approach using Simulated Annealing to optimize group formation in educational settings, aiming to improve the process based on students' academic records.

Contribution

It introduces a novel application of Simulated Annealing for optimal group formation, demonstrating significant success across multiple datasets.

Findings

High success rate in forming optimal groups

Effective use of academic records for clustering

Potential to revolutionize group formation processes

Abstract

Group formation itself a perplexing process. Over the decade of time education and others disciple has improved imminently but optimal group formation in educational system is still struggling. Our research focus on to create optimal group in a class of any institute. In this research we use Simulated Annealing (SA) for best group formation based on the previous academic record. We generally create an arbitrary cluster first then optimise using SA. Our model has significant success rate over a large number of datasets. This research will play a pioneer role in group formations in the academic and related researches.