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This content will become publicly available on June 14, 2026

Title: Demonstrating Replay for Highly Scalable and Cost-Effective User Research of Virtual Reality Learning Games
Observing users interacting with a learning game, often referred to as playtesting, is a critical component of usability testing. Unfortunately, this practice is expensive, requiring users and researchers to be in the same place at the same time as the participants. With Virtual Reality, this difficulty is amplified due to the experience being hidden from researcher view by default, and the extra complexity of setting up casting to an external monitor, especially with groups. In this paper we develop and utilize a replay-based approach to usability testing that relieves these concerns. The approach uses a low-bandwidth stream of telemetry signals that are generated by the original play session. These signals are then reconstructed into a full representation of the original experience at a different time or place and leveraged to identify usability issues. Once the issues have been discovered, automated processes are developed to computationally identify their presence and severity in arbitrarily large public audiences. This work contributes a demonstrated use of replay for Virtual Reality usability research, and a novel use of replay combined with educational data mining to develop an automated process for studying large audiences at low cost.  more » « less
Award ID(s):
2243668 1907384 2116046
PAR ID:
10609204
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
2025 11th International Conference of the Immersive Learning Research Network (iLRN) Proceedings - Selected Academic Contributions
Date Published:
Subject(s) / Keyword(s):
Replay VR Virtual Reality Educational Data Mining Usability Game Data
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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