Knowing Your Student: Targeted Teaching Decision Support Through Asymmetric Mixed Reality Collaborative Learning

Abstract

The Collaborative Virtual Environments (CVEs) created by Mixed Reality (MR) technologies have been classified as symmetric and asymmetric CVEs, where the latter aim to provide different authorities for different collaborator roles utilizing heterogeneous techniques that cover the full gamut of Milgram’s Mixed Reality continuum. The Light Field Display (LFD), as a new type of MR display that generates auto-stereoscopic viewing experience without head-mounted devices, has been incorporated with Augmented Reality (AR) and Virtual Reality (VR) headsets to create remote and co-located asymmetric collaborative environments. In previous asymmetric CVE research, LFDs were adapted to simultaneously render multi-contents for multiple students to lower down average device costs for the MR vet training. However, multiple students sharing one LFD to interact with the teacher may weaken the teacher’s understanding of individual student’s current learning progress, which may make teaching decisions even harder. Therefore, this paper presents an enhanced solution that supports teaching decisions targeted at each student without increasing the device costs. The context-aware LFD student clients, which render a dynamic viewing zone for each student by face encoding tracking, are implemented and applied for anti-cheat quiz support. By synchronizing each student’s tracking data with a Local Area Network (LAN) middleware, the AR teacher client can distinguish different students to in-situ superimpose the quiz progress and targeted-explainable teaching decision support over each corresponding student’s head. Ten University vet/anatomy teachers participated in the remote expert review study to provide professional feedback. According to the questionnaire results, they think the designed collaborative learning tool will be helpful for both teachers and students.

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X. Pan, M. Zheng, X. Xu and A. G. Campbell, “Knowing Your Student: Targeted Teaching Decision Support Through Asymmetric Mixed Reality Collaborative Learning,” in IEEE Access, vol. 9, pp. 164742-164751, 2021, doi: 10.1109/ACCESS.2021.3134589.