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

The Collaborative Virtual Environments (CVEs) created by Mixed Reality (MR) technologies have been classified as symmetric and asymmetric CVEs. The latter aim to provide different authorities for different collaborator roles utilizing heterogeneous techniques that cover the entire gamut of Milgram's Mixed Reality continuum. As a new type of MR display that generates an auto-stereoscopic viewing experience without head-mounted devices, the Light Field Display (LFD) 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 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 students' current learning progress, making 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.