Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study.

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Bibliographic Details
Title:	Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study.
Authors:	ANDRADE, Tiago Luís de¹ tiago@unemat.br, RIGO, Sandro José¹ rigo@unisinos.br, BARBOSA, Jorge Luis Victória¹ jbarbosa@unisinos.br
Source:	Informatics in Education. May2021, Vol. 20 Issue 2, p171-203. 33p.
Subject Terms:	Psychology of students, School dropouts, *Distance education, Data mining, Digital technology
Abstract:	Distance 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. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	Distance 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. [ABSTRACT FROM AUTHOR]
ISSN:	16485831
DOI:	10.15388/infedu.2021.09