A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics
Computers & Education Volume 58, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd
Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as may be desired, translating the grouping problem into one of multi-objective optimization. In order to validate our approach, an experiment was designed with 135 college freshmen considering three characteristics: an estimate of student knowledge levels, an estimate of student communicative skills, and an estimate of student leadership skills. Results of such an experiment allowed for the validation, not only from the computational point of view by measuring the algorithmic performance, but also from the pedagogical point of view by measuring student outcomes, and comparing them with two traditional group formation strategies: random and self-organized.
Moreno, J., Ovalle, D.A. & Vicari, R.M. (2012). A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers & Education, 58(1), 560-569. Elsevier Ltd.
- collaborative learning
- College Freshmen
- communication skills
- Cooperative learning
- Genetic Algorithms
- group formation
- Group Membership
- Heterogeneous Grouping
- Homogeneous Grouping
- Knowledge Level
- Multi-objective optimization
- Student Characteristics
- Student Leadership
Cited ByView References & Citations Map
Chih-Ming Chen, Graduate Institute of Library; Chi-Hsiung Kuo, E-learning Master Program of Library and Information Studies
Computers & Education Vol. 133, No. 1 (May 2019) pp. 94–115
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