In today’s educational institutions, student performance can vary widely due to differences in cognition, motivation, and environmental factors. These variations create challenges in achieving optimal learning outcomes. To address these challenges, Optimal Group Formation (OGF) has emerged as a promising research area. Optimal Group Formation (OGF) aims to form student groups that maximize learning efficiency based on past academic performance. Group formation problems are inherently complex and time-consuming, but their applications are extensive, spanning from manufacturing systems to educational contexts. This paper introduces a machine learning-based model designed to create optimal student groups using academic records as the primary input. The goal is to enhance overall group performance and reduce error rates by organizing students into cohesive, efficient teams. What sets this research apart is its focus on educational group formation, leveraging machine learning to improve collaborative learning outcomes. The paper also reviews prior research, emphasizing the importance of Optimal Group Formation (OGF) in various fields and its relevance in education. The model’s effectiveness is demonstrated through comparative analysis, showcasing its potential to improve group dynamics in both theoretical and lab-based courses. Ultimately, the aim is to improve educational outcomes by ensuring that student groups are optimally balanced and structured.
| Published in | Machine Learning Research (Volume 9, Issue 2) |
| DOI | 10.11648/j.mlr.20240902.13 |
| Page(s) | 48-53 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Optimal Group Information (OGI), Machine Learning (ML), Simulated Annealing (SA), Support Vector Machine (SVM)
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APA Style
Hasan, M., Babu, M. S., Emran, M. A. (2024). A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance. Machine Learning Research, 9(2), 48-53. https://doi.org/10.11648/j.mlr.20240902.13
ACS Style
Hasan, M.; Babu, M. S.; Emran, M. A. A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance. Mach. Learn. Res. 2024, 9(2), 48-53. doi: 10.11648/j.mlr.20240902.13
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Download@article{10.11648/j.mlr.20240902.13,
author = {Mahbub Hasan and Md. Shohel Babu and Md. Al Emran},
title = {A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance
},
journal = {Machine Learning Research},
volume = {9},
number = {2},
pages = {48-53},
doi = {10.11648/j.mlr.20240902.13},
url = {https://doi.org/10.11648/j.mlr.20240902.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20240902.13},
abstract = {In today’s educational institutions, student performance can vary widely due to differences in cognition, motivation, and environmental factors. These variations create challenges in achieving optimal learning outcomes. To address these challenges, Optimal Group Formation (OGF) has emerged as a promising research area. Optimal Group Formation (OGF) aims to form student groups that maximize learning efficiency based on past academic performance. Group formation problems are inherently complex and time-consuming, but their applications are extensive, spanning from manufacturing systems to educational contexts. This paper introduces a machine learning-based model designed to create optimal student groups using academic records as the primary input. The goal is to enhance overall group performance and reduce error rates by organizing students into cohesive, efficient teams. What sets this research apart is its focus on educational group formation, leveraging machine learning to improve collaborative learning outcomes. The paper also reviews prior research, emphasizing the importance of Optimal Group Formation (OGF) in various fields and its relevance in education. The model’s effectiveness is demonstrated through comparative analysis, showcasing its potential to improve group dynamics in both theoretical and lab-based courses. Ultimately, the aim is to improve educational outcomes by ensuring that student groups are optimally balanced and structured.
},
year = {2024}
}
TY - JOUR T1 - A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance AU - Mahbub Hasan AU - Md. Shohel Babu AU - Md. Al Emran Y1 - 2024/09/29 PY - 2024 N1 - https://doi.org/10.11648/j.mlr.20240902.13 DO - 10.11648/j.mlr.20240902.13 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 48 EP - 53 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20240902.13 AB - In today’s educational institutions, student performance can vary widely due to differences in cognition, motivation, and environmental factors. These variations create challenges in achieving optimal learning outcomes. To address these challenges, Optimal Group Formation (OGF) has emerged as a promising research area. Optimal Group Formation (OGF) aims to form student groups that maximize learning efficiency based on past academic performance. Group formation problems are inherently complex and time-consuming, but their applications are extensive, spanning from manufacturing systems to educational contexts. This paper introduces a machine learning-based model designed to create optimal student groups using academic records as the primary input. The goal is to enhance overall group performance and reduce error rates by organizing students into cohesive, efficient teams. What sets this research apart is its focus on educational group formation, leveraging machine learning to improve collaborative learning outcomes. The paper also reviews prior research, emphasizing the importance of Optimal Group Formation (OGF) in various fields and its relevance in education. The model’s effectiveness is demonstrated through comparative analysis, showcasing its potential to improve group dynamics in both theoretical and lab-based courses. Ultimately, the aim is to improve educational outcomes by ensuring that student groups are optimally balanced and structured. VL - 9 IS - 2 ER -
Computer Science and Engineering, Southeast University, Dhaka, Bangladesh
Computer Science and Engineering, Southeast University, Dhaka, Bangladesh
Computer Science and Engineering, Southeast University, Dhaka, Bangladesh
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