Work-in-Progress-Motion Tracking Data as a Proxy for Cognitive Load in Immersive Learning

Recent research has produced mixed results regarding the effectiveness of learning in VR. It has been suggested that the rich multisensory input in VR may induce cognitive overload that impedes the learning process. Cognitive load is typically measured by administering questionnaires. Although quest...

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Bibliographic Details
Published in2022 8th International Conference of the Immersive Learning Research Network (iLRN) pp. 1 - 3
Main Authors Moser, Ivan, Comsa, Ioan-Sorin, Parsaeifard, Behnam, Bergamin, Per
Format Conference Proceeding
LanguageEnglish
Published Immersive Learning Research Network 30.05.2022
Subjects
cognitive load
Education
Headphones
learning analytics
motion tracking
N-back task
neural networks
Physiology
Real-time systems
Recurrent neural networks
Task analysis
Tracking
virtual reality
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Summary:Recent research has produced mixed results regarding the effectiveness of learning in VR. It has been suggested that the rich multisensory input in VR may induce cognitive overload that impedes the learning process. Cognitive load is typically measured by administering questionnaires. Although questionnaires are easily used, they imply the need to interrupt students during learning or to assess cognitive load in retrospect. In this work-in-progress paper, we argue that VR motion tracking data has the potential to provide unobtrusive, yet valid measures of cognitive load. We report preliminary results from a user study that aims at predicting cognitive load using the tracking data of a VR headset and two hand controllers. Using a recurrent neural network, we were able to distinguish between different levels of cognitive load with an accuracy of more than 88 percent. Based on this finding, we reflect on future research directions and practical considerations.
DOI:10.23919/iLRN55037.2022.9815894