Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Learning Analytics Theoretical Framework for Virtual Reality Instructional Applications

Version 1 : Received: 6 October 2020 / Approved: 8 October 2020 / Online: 8 October 2020 (13:02:32 CEST)

How to cite: Christopoulos, A.; Pellas, N.; Laakso, M. A Learning Analytics Theoretical Framework for Virtual Reality Instructional Applications. Preprints 2020, 2020100176 (doi: 10.20944/preprints202010.0176.v1). Christopoulos, A.; Pellas, N.; Laakso, M. A Learning Analytics Theoretical Framework for Virtual Reality Instructional Applications. Preprints 2020, 2020100176 (doi: 10.20944/preprints202010.0176.v1).

Abstract

The commercial popularity of Virtual Reality attracted educators’ interest and brought new opportunities to the educational landscape. At the same time, Learning Analytics emerged with the promise to revolutionise the traditional practices by introducing ways to systematically assess and improve the effectiveness of instruction. However, the collection of ‘big’ educational data is mostly associated with web-based platforms as they offer direct access to learners’ activities with minimal effort. On the antipode, the nature of VR limits the opportunities for such data collection. Hence, in the context of this work, we present a four-dimensional theoretical framework, that accounts the information that can be gathered from VR-supported instruction, and propose a set of structural elements which can be utilised for the development of a Learning Analytics prototype system. The outcomes of this work are expected to support practitioners to maximise the potential of their interventions and provide inspiration for new ones.

Subject Areas

Virtual Reality; Learning Analytics; STEM Education; Instructional Design.

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