An Enhanced Genetic Algorithm for Heterogeneous Group Formation Based on Multi-Characteristics in Social-Networking-Based Learning

Autor/inn/en	Krouska, Akrivi; Virvou, Maria
Titel	An Enhanced Genetic Algorithm for Heterogeneous Group Formation Based on Multi-Characteristics in Social-Networking-Based Learning
Quelle	In: IEEE Transactions on Learning Technologies, 13 (2020) 3, S.465-476 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Krouska, Akrivi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2019.2927914
Schlagwörter	Social Networks; Cooperative Learning; Computer Uses in Education; Grouping (Instructional Purposes); Mathematics; Social Media; College Students; Foreign Countries; Automation; Student Attitudes; Greece + Suchen Sie Ihr Suchwort? Social network; Soziales Netzwerk; Kooperatives Lernen; Computernutzung; Grouping; Gruppenbildung; Mathematik; Soziale Medien; Collegestudent; Ausland; Schülerverhalten; Griechenland
Abstract	Social networking-based learning (SN-learning) is one of the most promising innovations to promote learning via a social network, and thus, providing a more interactive, student-centered, cooperative, and on-demand environment. In such an environment, group formation plays an important role to the effectiveness of learning process. Adequate groups foster student interactions and increase learning outcomes. However, group formation is a complex task and requires automatic approaches to produce the optimal results in short time. To this direction, this paper presents a novel genetic algorithm for student grouping in an SN-learning system. Its innovations pertain to the attributes used for the composition of groups and genetic operators applied. In particular, student attributes refer to the three main dimensions of learning in an SN-learning environment: academic, cognitive, and social. Regarding genetic operators, the algorithm performs two crossover operators: a modification of two-point crossover and a new approach, called one-point per group crossover. Evaluating the proposed algorithm performance, the results show that it is more efficient than simple genetic algorithm approach, and considers a larger number of parameters than usual. Moreover, from the pedagogical perspective, a positive students' attitude and high acceptance toward our group formation method is indicated. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01