An Algorithm Based on An Efficient Cost Model to Form Learning Groups
DOI:
https://doi.org/10.47839/ijc.23.3.3666Keywords:
Group formation Algorithm, Learning group formation, Intra-group Homogeneity, Inter-group Homogeneity, Generalized Assignment problem, Cost model, Reference valueAbstract
The purpose of this research is to form learning groups that are intra-homogeneous (a high level of similarity across student GPAs inside a group), inter-homogeneous (similarity or balance in the degree of homogeneity between groups), and balanced in size. The algorithm proposed for this purpose treats the learning group formation as an assignment-type optimization problem where it seeks to find a feasible least-cost assignment of a given set of students to a given set of learning groups. It is referred to as GAGF (Generalized Assignment Strategy for Group Formation). It is based on an efficient cost model, which performs three tasks: measuring the cost of assigning students to a learning group, relating each improvement in assignment cost to increased intra-group homogeneity and group size balance, and bringing the intra-homogeneity of the groups to a reference value (a specific level of homogeneity), which improves inter-homogeneity. Experimental results have shown that the GAGF algorithm is effective at constructing intra- and inter-homogeneous learning groups with balanced sizes. It was found that using GAGF attained an improvement of more than 29% in intra-group homogeneity when compared to both related work and self-formation methods. It significantly improved inter-group homogeneity, outperforming related works by 79.75%.
References
O. Revelo-Sánchez, C. A. Collazos & M. A. Redondo, “Group formation in collaborative learning contexts based on personality traits: An empirical study in initial programming courses,” Interaction Design and Architecture(s) Journal - IxD&A, no. 49, 2, 2021. https://doi.org/10.55612/s-5002-049-002.
J. Moreno, D. A. Ovalle & R. M. Vicari, “A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics,” Computers & Education, vol. 58, issue 1, pp. 560–569, 2012. https://doi.org/10.1016/j.compedu.2011.09.011.
V. Gherheș, C. E. Stoian, M. A. Fărcașiu, M. Stanici, “E-Learning vs face-to-face learning: analyzing students’ preferences and behaviors,” Sustainability, vol. 13, no. 8, pp. 4381, 2021. https://doi.org/10.3390/su13084381.
R. J. Petillion, W. S. McNeil, “Student experiences of emergency remote teaching: impacts of instructor practice on student learning, engagement, and well-being,” J Chem Educ., vol. 97, pp. 2486–2493, 2020. https://doi.org/10.1021/acs.jchemed.0c00733.
P. Photooulos, C. Tsonos, I. Stavrakas, D. Triantis, “Remote and in-person learning: Utility versus social experience,” SN Comput. Sci., vol. 4, no. 116, pp. 1–13, 2023. https://doi.org/10.1007/s42979-022-01539-6.
A. Ben Ammar, A. Minalla, “An algorithm based on self-balancing binary search tree to generate balanced, intra-homogeneous and inter-homogeneous learning groups,” International Journal of Advanced Computer Science and Applications, vol. 14, issue 6, 2023. https://doi.org/10.14569/IJACSA.2023.0140622.
O. Revelosanchez, C. A. Collazos, M. A. Redondo, & I. I. Bittencourt, “Homogeneous group formation in collaborative learning scenarios: An approach based on personality traits and genetic algorithms,” IEEE Trans. Learn. Technol., 2021. https://doi.org/10.1109/TLT.2021.3105008.
C. T. Krouska, M. Virvou, “Applying genetic algorithms for student grouping in collaborative learning: A synthetic literature review,” Intelligent Decision Technologies, vol. 13, pp. 395-406, 2020. Doi: 10.3233/IDT-190184. https://doi.org/10.3233/IDT-190184.
F. Fahmi & D. Nurjanah, “Group formation using multi objectives ant colony system for collaborative learning,” Proceedings of the 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Malang, Indonesia, October 2018, pp. 696-702, https://doi.org/10.1109/EECSI.2018.8752690.
V. R. Garcia, B. Vega, A. Ruiz-Ichazu, D. Rivera, E. Rosero-Perez, “Automating the generation of study teams through genetic algorithms based on learning styles in higher education,” Advances in Artificial Intelligence, Software and Systems Engineering, pp. 270-277, 2021. https://doi.org/10.1007/978-3-030-51328-3_38.
C.-M. Chen, C.-H. Kuo, “An optimized group formation scheme to promote collaborative problem-based learning,” Computers & Education, vol. 133, pp. 94–115, 2019. https://doi.org/10.1016/j.compedu.2019.01.011.
U. Haq, A. Anwar, I. U. Rehman, W. Asif, D. Sobnath, H. H. Sherazi, et al., “Dynamic group formation with intelligent tutor collaborative learning: A novel approach for next generation collaboration,” IEEE Access, vol. 9 , 2021, 143406-143422. https://doi.org/10.1109/ACCESS.2021.3120557.
P. K. Imbrie, J. Agarwal, G. Raju, “Genetic algorithm optimization of teams for heterogeneity,” Proceedings of the IEEE Frontiers in Education Conference (FIE), Uppsala, Sweden, October 2020, pp. 1–5. https://doi.org/10.1109/FIE44824.2020.9274243.
D. Lambić, B. Lazović, A. Djenić & M. Marić, “A novel metaheuristic approach for collaborative learning group formation,” Journal of Computer Assisted Learning, vol. 34, issue 6, pp. 907–916, 2018. https://doi.org/10.1111/jcal.12299.
X. Li, F. Ouyang, W. Chen, “Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions,” J Comput High Educ, vol. 34, pp. 790–819, 2022. https://doi.org/10.1007/s12528-022-09321-6.
Y. Lin, Y. Chang, C. Chu, “Novel approach to facilitating tradeoff multi-objective grouping optimization,” IEEE Transactions on Learning Technologies, vol. 9, issue 2, pp. 107–119, 2016. https://doi.org/10.1109/TLT.2015.2471995.
H. L. Masri, K. S. Kalid, “Group-formation system to facilitate heterogeneous grouping in collaborative learning for non-technical courses,” Platform A J. Sci. Technol., vol. 3, no. 1, pp. 48-62, 2020. https://doi.org/10.61762/pjstvol3iss1art7130.
R. C. Reis, S. Isotani, C. L. Rodriguez, K. T. Lyra, P. A. Jaques, I. I. Bittencourt, “Affective states in computer-supported collaborative learning: Studying the past to drive the future,” Computers & Education, vol. 120, pp. 29–50, 2018. https://doi.org/10.1016/j.compedu.2018.01.015.
Đ. Takači, M. Marić, G. Stankov, A. Djenić, “Efficiency of using VNS algorithm for forming heterogeneous groups for CSCL learning,” Computers & Education, no. 109, pp. 98–108, 2017. https://doi.org/10.1016/j.compedu.2017.02.014.
E. Andrejczuk, F. Bistaffa, C. Blum, J.A. Rodriguez-Aguilar, C. Sierra, “Heterogeneous teams for homogeneous performance,” Proceedings of the Conference on Principles and Practice of Multi- Agent Systems PRIMA 2018, Lecture Notes in Computer Science, Springer, Cham, Switzerland, 2018, pp. 89–105. https://doi.org/10.1007/978-3-030-03098-8_6.
S. Garshasbi, Y. Mohammadi, S. Graf, S. Garshasbi, J. Shen, “Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity,” Expert Systems with Applications, vol. 118, pp. 506–521, 2019. https://doi.org/10.1016/j.eswa.2018.10.034.
P. B. C. Miranda, R. F. Mello, C.A. Nascimento, “A multi-objective optimization approach for the group formation problem,” Expert Systems with Applications, vol. 162, pp. 113828, 2020. https://doi.org/10.1016/j.eswa.2020.113828.
Z. Sun, M. Chiarandini, “An exact algorithm for group formation to promote collaborative learning,” Proceedings of the 11th Int. Learn. Anal. Knowl. Conf., 2021, pp. 546-552. https://doi.org/10.1145/3448139.3448196.
M. Hasan, “Optimal Group Formulation Using Machine Learning,” arXiv preprint arXiv:2105.07858. 2021.
C. T. Krouska & M. Virvou, “An enhanced genetic algorithm for heterogeneous group formation based on multi-characteristics in social networking-based learning,” IEEE Transactions on Learning Technologies, vol. 13, issue 3, pp. 465–476, 2020. https://doi.org/10.1109/TLT.2019.2927914.
N. Sarode & J. Bakal, “Toward effectual group formation method for collaborative learning environment,” Sustainable Communication Networks and Application, Chennai, India:Springer, 2021, pp. 351-361. https://doi.org/10.1007/978-981-15-8677-4_29.
J. M. A. Pinninghoff, A. R. Contreras, L. P. Salcedo et al., “Genetic algorithms as a tool for structuring collaborative groups,” Nat Comput, vol. 16, pp. 231–239, 2017. https://doi.org/10.1007/s11047-016-9574-1.
Z. Yaqian, L. Chunrong, L. Shiyu, L. Weigang. “An improved genetic approach for composing optimal collaborative learning groups”. Knowledge-Based Systems, vol. 139, pp. 214-225, 2018, https://doi.org/10.1016/j.knosys.2017.10.022.
B. Jong, Y. Wu, T. Chan. “Dynamic grouping strategies based on a conceptual graph for cooperative learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, issue 6, pp. 738–747, 2006. https://doi.org/10.1109/TKDE.2006.93.
H.-W. Tien, Y.-S. Lin, Y.-C. Chang, & C.-P. Chu, “A genetic algorithm-based multiple characteristics grouping strategy for collaborative learning,” Proc. Int. Conf. Web Learn., 2013, pp. 11-22. https://doi.org/10.1007/978-3-662-46315-4_2.
R. Costaguta, “Algorithms and machine learning techniques in collaborative group formation,” In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and its Applications. MICAI 2015. Lecture Notes in Computer Science, Springer, Cham, vol. 9414, 2015, https://doi.org/10.1007/978-3-319-27101-9_18.
R. Vankayalapati, K. Ghutugade, R. Vannapuram, and B. Prasanna, “K-means algorithm for clustering of learners performance levels using machine learning techniques,” Revue d'Intelligence Artificielle, vol. 35, pp. 99-104, 2021. https://doi.org/10.18280/ria.350112.
M. Hodara, K. Lewis, How well does high school grade point average predict college performance by student urbanicity and timing of college entry? US Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northwest, 2017. [Online]. Available at: https://ies.ed.gov/ncee/edlabs/projects/project.asp?projectID=4546.
K. Singh, & T. Maloney, “Using validated measures of high school academic achievement to predict university success,” New Zealand Economic Papers, vol. 53, issue 1, pp. 89–106. https://doi.org/10.1080/00779954.2017.1419502.
M. M. Sulphey, N. S. Al-Kahtani, A. M. Syed, “Relationship between admission grades and academic achievement,” The International Journal of Entrepreneurship and Sustainability Issues, vol. 5, issue 3, pp. 648–658, 2018. https://doi.org/10.9770/jesi.2018.5.3(17).
R. Stephens, Essential Algorithms, 2nd edition, Wiley, 2019. ISBN: 9781119575993. https://doi.org/10.1002/9781119575955.
S. O. Adodo, J. O. Agbayewa, “Effect of homogenous and heterogeneous ability grouping class teaching on student’s interest, attitude and achievement in integrated science,” International Journal of Psychology and Counseling, vol. 3, issue 3, pp. 48-54, 2011.
A. S. Booij, E. Leuven, H. Oosterbeek, “Ability peer effects in university: Evidence from a randomized experiment,” Rev. Econ. Stud., vol. 84, pp. 547–578, 2017. https://doi.org/10.1093/restud/rdw045.
O. E. Kundakcioglu, S. Alizamir, “Generalized assignment problem,” In: Floudas, C., Pardalos, P. (eds) Encyclopedia of Optimization. Springer, Boston, MA., 2009, pp. 1153-1162. ISBN 978-0-387-74759-0, https://doi.org/10.1007/978-0-387-74759-0_200.
S. Martello, P. Toth, “An algorithm for the generalized assignment problem,” Proceedings of the 9th IFORS Conference, Hamburg, Germany, 1981.
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