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Autor/inn/ende Andrade, Tiago Luís; Rigo, Sandro José; Barbosa, Jorge Luis Victória
TitelActive Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study
QuelleIn: Informatics in Education, 20 (2021) 2, S.171-203 (33 Seiten)
PDF als Volltext kostenfreie Datei    Verfügbarkeit 
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1648-5831
SchlagwörterLearning Analytics; Distance Education; Dropout Prevention; Data Analysis; Active Learning; Virtual Classrooms; Dropouts; Educational Research
AbstractDistance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies' application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students' abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout. [Note: The page range (171-204) shown on the PDF is incorrect. The correct page range is p171-203.] (As Provided).
AnmerkungenVilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2022/1/01
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