An exact algorithm for group formation to promote collaborative learning

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Abstract

Collaborative learning has been widely used to foster students' communication and joint knowledge construction. However, the classification of learners into well-structured groups is one of the most challenging tasks in the field. The aim of this study is to propose a novel method to form intra-heterogeneous and inter-homogeneous groups based on relevant student characteristics. Such a method allows for the consideration of multiple student characteristics and can handle both numerical and categorical characteristic types simultaneously. It assumes that the teacher provides an order of importance of the characteristics, then it solves the grouping problem as a lexicographic optimization problem in the given order. We formulate the problem in mixed integer linear programming (MILP) terms and solve it to optimality. A pilot experiment was conducted with 29 college freshmen considering three general characteristics (i.e., 13 specific features) including knowledge level, demographic information, and motivation. Results of such an experiment demonstrate the validity and computational feasibility of the algorithmic approach. Large-scale studies are needed to assess the impact of the proposed grouping method on students' learning experience and academic achievement.

OriginalsprogEngelsk
TitelLAK 2021 Conference Proceedings - The Impact we Make : The Contributions of Learning Analytics to Learning, 11th International Conference on Learning Analytics and Knowledge
ForlagAssociation for Computing Machinery
Publikationsdato12. apr. 2021
Sider546-552
ISBN (Elektronisk)9781450389358
DOI
StatusUdgivet - 12. apr. 2021
Begivenhed11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021 - Virtual, Online, USA
Varighed: 12. apr. 202116. apr. 2021

Konference

Konference11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021
Land/OmrådeUSA
ByVirtual, Online
Periode12/04/202116/04/2021

Bibliografisk note

Publisher Copyright:
© 2021 ACM.

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