Research Article | | Peer-Reviewed

A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance

Received: 2 September 2024     Accepted: 19 September 2024     Published: 29 September 2024
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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.

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

Keywords

Optimal Group Information (OGI), Machine Learning (ML), Simulated Annealing (SA), Support Vector Machine (SVM)

References
[1] Luis E Agustín-Blas et al. "A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups". In: Expert Syst. Appl. 36 (Apr. 2009), pp. 7234-7241.
[2] Luis E Agustín-Blas et al. "Team formation based on group technology: A hybrid grouping genetic algorithm approach". In: Computers & Operations Research 38.2 (2011), pp. 484-495.
[3] Leena N Ahmed, Ender Özcan, and Ahmed Kheiri. "Solving High School Timetabling Problems Worldwide Using Selection Hyper-heuristics". In: Expert Systems with Applications 42 (Aug. 2015), pp. 5463-5471.
[4] Mohammed Alswaitti, Mohanad Albughdadi, and Nor Ashidi Mat Isa. "Density-based Particle Swarm Optimization Algorithm for Data Clustering". In: Expert Systems with Applications 91 (Sept. 2017).
[5] Mustafa Avci and Seyda Topaloglu. "A Hybrid Metaheuristic Algorithm for Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery". In: Expert Systems with Applications 53 (Jan. 2016).
[6] Kadir Buyukozkan et al. "Lexicographic Bottleneck Mixed-model Assembly Line Balancing Problem: Artificial Bee Colony and Tabu Search Approaches with Optimised Parameters". In: Expert Systems with Applications 50 (Dec. 2015).
[7] James Chen et al. "Assembly line balancing in garment industry". In: Expert Systems with Applications 39 (Sept. 2012), pp. 10073-10081.
[8] Wilmax Marreiro Cruz and Seiji Isotani. "Group Formation Algorithms in Collaborative Learning Contexts: A Systematic Mapping of the Literature". In: Collaboration and Technology. Ed. by Nelson Baloian et al. Cham: Springer International Publishing, 2014, pp. 199-214. ISBN: 978-3-319-10166-8.
[9] Sittipong Dantrakul, Chulin Likasiri, and Radom Pongvuthithum. "Applied p-median and p-center algorithms for facility location problems". In: Expert Systems with Applications 41 (June 2014), pp. 3596-3604.
[10] Tuanhung Dao, Seung Ryul Jeong, and Hyunchul Ahn. "A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach". In: Expert Syst. Appl. 39 (Feb. 2012), pp. 3731-3739.
[11] Ibrahim Dogan. "Analysis of facility location model using Bayesian Networks". In: Expert Systems with Applications: An International Journal 39 (Jan. 2012), pp. 1092-1104.
[12] Soheila Garshasbi et al. "Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity". In: Expert Systems with Applications 118 (2019), pp. 506-521. ISSN: 0957-4174.
[13] Mahbub Hasan and Md. Hasan Tarque. "An Efficient Predictive Weighted Algorithm for Students Performance Prediction". In: International Journal of Emerging Technology and Advanced Engineering 8.11 (2018), pp. 94-98. ISSN: 2250-2459. URL:
[14] Umair ul Hassan and Edward Curry. "Efficient Task Assignment for Spatial Crowdsourcing: A Combinatorial Fractional Optimization Approach with Semi-bandit Learning". In: Expert Systems with Applications 58 (Apr. 2016).
[15] Kaj Holmberg. "Formation of student groups with the help of optimisation". In: Journal of the Operational Research Society 70. 9 (2019), pp. 1538-1553.
[16] Saida Ishak Boushaki, Nadjet Kamel, and Omar Bendjeghaba. "A new quantum chaotic cuckoo search algorithm for data clustering". In: Expert Systems with Applications 96 (Dec. 2017).
[17] Fariborz Jolai et al. "An Electromagnetism-like algorithm for cell formation and layout problem". In: Expert Syst. Appl. 39 (Feb.2012), pp. 2172-2182.
[18] Iraj Mahdavi et al. "Genetic algorithm approach for solving a cell formation problem in cellular manufacturing". In: Expert Systems with Applications 36 (Apr. 2009), pp. 6598-6604.
[19] Borja Menéndez et al. "Variable Neighborhood Search strategies for the Order Batching Problem". In: Computers & Operations Research78 (Feb.2016).
[20] Michael Mutingi and Charles Mbohwa. Grouping Genetic Algorithms. Vol. 666. Jan. 2017. ISBN: 978- 3-319-44393-5.
[21] Mehdi Rashidnejad, Sadoullah Ebrahimnejad, and Jalal Safari. "A bi-objective model of preventive maintenance planning in distributed systems considering vehicle routing problem". In: Computers & Industrial Engineering 120 (May 2018).
[22] Yeliz Buruk Sahin and Serafettin Alpay. "A metaheuristic approach for a cubic cell formation problem". In: Expert Systems with Applications 65 (Aug. 2016), pp. 40-51.
[23] Anna Sapienza, Palash Goyal, and Emilio Ferrara. "Deep Neural Networks for Optimal Team Composition". In: Frontiers in Big Data 2 (2019), p. 14. ISSN: 2624-909X. URL:
[24] André Scholz, Daniel Schubert, and Gerhard Wäscher. "Order picking with multiple pickers and due dates. Simultaneous solution of Order Batching, Batch Assignment and Sequencing, and Picker Routing Problems". In: European Journal of Operational Research 263 (Apr. 2017).
[25] Vassilios Skoullis and Ioannis Tassopoulos. "Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm". In: Applied Soft Computing 52 (2017) (Nov. 2016), pp. 277-289.
[26] Kalliopi Tourtoglou and Maria Virvou. "Simulated Annealing in Finding Optimum Groups of Learners of UML". In: Intelligent Interactive Multimedia Systems and Services. Ed. by George A. Tsihrintzis et al. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 147-156. ISBN: 978-3-642-14619-0. URL:
[27] Bo Wang and Xiaohua Xia. "A Preliminary Study on the Robustness of Grouping Based Maintenance Plan Optimization in Building Retrofitting". In: Energy Procedia 105 (May 2017), pp. 3308-3313.
[28] Tanachapong Wangkhamhan, Sirapat Chiewchanwattana, and Khamron Sunat. "Efficient algorithms based on the k-means and Chaotic League Championship Algorithm for numeric, categorical, and mixed-type data clustering". In: Expert Systems with Applications 90 (Aug. 2017).
[29] Hyeongon Wi et al. "A team formation model based on knowledge and collaboration". In: Expert Systems with Applications 36 (July 2009), pp. 9121-9134.
Cite This Article
  • 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

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    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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    AMA Style

    Hasan M, Babu MS, Emran MA. 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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  • @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}
    }
    

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    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
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    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
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