Abstract
Observing behavior can provide insights into mental states and cognitive processes. In the context of expertise measurement, research is taking advantage of analyzing gaze-based data to understand those underlying processes. The movements of the eyes represent search strategies in problem-solving scenarios, which can be used to distinguish a novice from a person that is more experienced with the topic and the solving process. Applications such as learning environments can be improved by taking expertise into account, as expertise-related instructions and design-decisions could then be adjusted to the learner’s individual needs. In the following, we will discuss the meaning of expertise and instruction design for learning in more detail. Prior work has shown that different groups of expertise can be distinguished using eye movements. We verify this for virtual reality-based chess trainings by presenting a study on chess problem solving in VR. Based on these findings, we discuss suggestions for the implementation of adaptive learning applications on the example of chess.
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Hainke, C., Pfeiffer, T. (2021). Eye Movements in VR Training: Expertise Measurement and it’s Meaning for Adaptive Chess Training. In: Ahram, T.Z., Falcão, C.S. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2021. Lecture Notes in Networks and Systems, vol 275. Springer, Cham. https://doi.org/10.1007/978-3-030-80091-8_15
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DOI: https://doi.org/10.1007/978-3-030-80091-8_15
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