Learning Analytics in Flipped Classrooms: A Scoping Review

Autor/inn/en	Algayres, Muriel; Triantafyllou, Evangelia
Titel	Learning Analytics in Flipped Classrooms: A Scoping Review
Quelle	In: Electronic Journal of e-Learning, 18 (2020) 5, S.397-409 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1479-4403
Schlagwörter	Learning Analytics; Flipped Classroom; Literature Reviews; Best Practices; Technology Uses in Education; Outcomes of Education; Learner Engagement; Integrated Learning Systems + Suchen Sie Ihr Suchwort? Flipped classrooms; Flip teaching; Inverted teaching; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Lernleistung; Schulerfolg
Abstract	The Flipped Classroom (FC) is an instruction method, where the traditional lecture and homework sessions are inverted. Online material is given to students in order to gain necessary knowledge before class, while class time is devoted to application of this knowledge and reflection. The hypothesis is that there could be deep and creative discussions when teacher and students physically meet, which has known a significant surge of popularity in the past decade. A marked recent trend in the FC is the increased use of Learning Analytics (LA) to support the development of the FC and students' reflexive learning. The aim of this paper is to investigate the literature on applications of LA in FCs, and to determine the best practices and needs for technological development supporting LA in the FC by means of a scoping review. This literature review revealed that there is potential in using LA in the FC, especially as a means to predict students' learning outcome and to support adaptive learning and improvement on the curriculum. However, further long-term studies and development is necessary to encourage self-directed learning in students and to develop the whole of the FC for a more diverse population of students. We anticipate an increased and expanded use of LA to come, with focus on predictive and prescriptive analytics providing more adaptive learning experience. We also anticipate that LA will expand beyond data mining to correlate student performance and online engagement with the aim to include a wider range of possibilities of interventions and adaptation of the learning experience. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01