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In this paper, we introduce two remote extended reality (XR) research methodsthat can overcome the limitations of lab-based controlled experiments,especially during the COVID-19 pandemic: (1) a predictive model-based taskanalysis and (2) a large-scale video-based remote evaluation. We used a boxstacking task including three interaction modalities - two multimodalgaze-based interactions as well as a unimodal hand-based interaction which isdefined as our baseline. For the first evaluation, a GOMS-based task analysiswas performed by analyzing the tasks to understand human behaviors in XR andpredict task execution times. For the second evaluation, an online survey wasadministered using a series of the first-person point of view videos where auser performs the corresponding task with three interaction modalities. A totalof 118 participants were asked to compare the interaction modes based on theirjudgment. Two standard questionnaires were used to measure perceived workloadand the usability of the modalities.

Model-based Task Analysis and Large-scale Video-based Remote Evaluation Methods for Extended Reality Research