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Autor/inn/enMoreno, Julian; Ovalle, Demetrio A.; Vicari, Rosa M.
TitelA Genetic Algorithm Approach for Group Formation in Collaborative Learning Considering Multiple Student Characteristics
QuelleIn: Computers & Education, 58 (2012) 1, S.560-569 (10 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0360-1315
DOI10.1016/j.compedu.2011.09.011
SchlagwörterCollege Freshmen; Genetics; Mathematics; Models; Cooperative Learning; Heterogeneous Grouping; Homogeneous Grouping; Student Characteristics; Knowledge Level; Communication Skills; Student Leadership; Computation; Selection; Group Membership
AbstractConsidering 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. (Contains 3 figures and 8 tables.) (As Provided).
AnmerkungenElsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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