Evaluating Similarity Measures for Gaze Pattern on the Context of Representational Competence in Physics Education

Seyyed Saleh Mozaffari Chanijani, Pascal Klein, Jouni Viiri, Sheraz Ahmed, Jochen Kuhn, Andreas Dengel

In: ETRA '18: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications. Symposium on Eye Tracking Research & Applications (ETRA) June 14-17 Warsaw Poland Pages 1-5 ISBN 978-1-4503-5706-7 Association for Computing Machinery 2018.


The competent handling of representations is required for understanding physics' concepts, developing problem-solving skills, and achieving scientific expertise. Using eye-tracking methodology, we present the contributions of this paper as follows: We first investigated the preferences of students with the different levels of knowledge; experts, intermediates, and novices, in representational competence in the domain of physics problem-solving. It reveals that experts more likely prefer to use vector than other representations. Besides, a similar tendency of table representation usage was observed in all groups. Also, diagram representation has been used less than others. Secondly, we evaluated three similarity measures; Levenshtein distance, transition entropy, and Jensen-Shannon divergence. Conducting Recursive Feature Elimination technique suggests Jensen-Shannon divergence is the best discriminating feature among the three. However, investigation on mutual dependency of the features implies transition entropy mutually links between two other features where it has mutual information with Levenshtein distance (Maximal Information Coefficient = 0.44) and has a correlation with Jensen-Shannon divergence (r(18313) = 0.70, p < .001).

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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz