Social Learning Analytics for Determining Learning Styles in a Smart Classroom

Autor/inn/en	Aguilar, J.; Buendia, O.; Pinto, A.; Gutiérrez, J.
Titel	Social Learning Analytics for Determining Learning Styles in a Smart Classroom
Quelle	In: Interactive Learning Environments, 30 (2022) 2, S.245-261 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Aguilar, J.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2019.1651745
Schlagwörter	Learning Analytics; Cognitive Style; Socialization; Social Networks; Data Use; Computer Assisted Instruction; Network Analysis; Learning Processes; Teaching Methods; Semantics; Task Analysis; Integrated Learning Systems; Computer Software; Law Students; Computer Science Education; Models; Higher Education + Suchen Sie Ihr Suchwort? Cognitive styles; Kognitiver Stil; Socialisation; Sozialisation; Social network; Soziales Netzwerk; Computer based training; Computerunterstützter Unterricht; Netzplantechnik; Learning process; Lernprozess; Teaching method; Lehrmethode; Unterrichtsmethode; Semantik; Aufgabenanalyse; Computer science lessons; Informatikunterricht; Analogiemodell; Hochschulbildung; Hochschulsystem; Hochschulwesen
Abstract	Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart classroom. Specifically, this paper proposes to determine the learning styles of the students in a smart classroom using SLA. In this proposal is analyzed external data from the web and social networks to build knowledge models about the students, in order to improve the learning processes that occur in the smart classrooms. In general, these SLA tasks will be organized in autonomous cycles, in order to integrate them with each other. The autonomic cycle will automate the execution of those tasks and the generation of knowledge models, in such a way to permanently monitor the learning process, observing it, analyzing it and determining the student learning styles. For the development of the SLA tasks, we will use concepts from the Semantic Mining, Text Mining, Data Mining, among other domains. Finally, we experiment in a test scenario, with results very interesting. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01