Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2022
Abstract
In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements of VR platform to the three psychological needs of learners as defined in the Self-Determination Theory (SDT) framework. We report on a VR environment that we developed for an undergraduate ‘Data Structures and Algorithms’ course. Incorporating the SelfDetermination Theory (SDT) framework, our VR environment aims to facilitate learners’ self-motivation in learning computing concepts. Built using Unity game engine, XR interaction toolkit, VRChat SDK3 and Oculus Quest 2 headsets, our VR environment was tested with focus group students who provided in-depth feedback on many aspects of the VR environment.
Keywords
computing education, virtual reality, immersive classroom
Discipline
Artificial Intelligence and Robotics | Educational Technology | Graphics and Human Computer Interfaces | Higher Education
Research Areas
Intelligent Systems and Optimization; Software and Cyber-Physical Systems
Publication
2022 IEEE International Conference on Teaching, Assessment, and Learning for Engineering: Hong Kong, December 4-7: Proceedings
First Page
1
Last Page
8
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
PANG, Shawn; SHIM, Kyong Jin; LAU, Yi Meng; and GOTTIPATI, Swapna.
VR Computing Lab: An immersive classroom for computing learning. (2022). 2022 IEEE International Conference on Teaching, Assessment, and Learning for Engineering: Hong Kong, December 4-7: Proceedings. 1-8.
Available at: https://ink.library.smu.edu.sg/sis_research/7586
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Educational Technology Commons, Graphics and Human Computer Interfaces Commons, Higher Education Commons