ARLEAN: An Augmented Reality Learning Analytics Ethical Framework

The emergence of the Learning Analytics (LA) field contextualised the connections in various disciplines and the educational sector, acted as a steppingstone toward the reformation of the educational scenery, thus promoting the importance of providing users with adaptive and personalised learning ex...

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Bibliographic Details
Published inComputers (Basel) Vol. 10; no. 8; p. 92
Main Authors Christopoulos, Athanasios, Mystakidis, Stylianos, Pellas, Nikolaos, Laakso, Mikko-Jussi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2021
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ISSN2073-431X
2073-431X
DOI10.3390/computers10080092

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Summary:The emergence of the Learning Analytics (LA) field contextualised the connections in various disciplines and the educational sector, acted as a steppingstone toward the reformation of the educational scenery, thus promoting the importance of providing users with adaptive and personalised learning experiences. At the same time, the use of Augmented Reality (AR) applications in education have been gaining a growing interest across all the educational levels and contexts. However, the efforts to integrate LA techniques in immersive technologies, such as AR, are limited and scarce. This inadequacy is mainly attributed to the difficulties that govern the collection and interpretation of the primary data. To deal with this shortcoming, we present the “Augmented Reality Learning Analytics” (ARLEAN) ethical framework, tailored to the specific characteristics that AR applications have, and focused on various learning subjects. The core of this framework blends the technological, pedagogical, and psychological elements that influence the outcome of educational interventions, with the most widely adopted LA techniques. It provides concrete guidelines to educational technologists and instructional designers on how to integrate LA into their practices to inform their future decisions and thus, support their learners to achieve better results.
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers10080092