Paper Info
Title
Evaluating similarity measures for gaze patterns in the context of representational competence in physics education
Abstract
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).
Year
DOI
Venue
2018
10.1145/3204493.3204564
2018 ACM SYMPOSIUM ON EYE TRACKING RESEARCH & APPLICATIONS (ETRA 2018)
Keywords
DocType
ISBN
physics, representational competence, eye-tracking, gaze patterns, similarity measures, feature selection
Conference
978-1-4503-5706-7
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Saleh Mozaffari100.34
Pascal Klein202.03
Jouni Viiri312.38
Sheraz Ahmed410528.32
Jochen Kuhn5228.28
Andreas Dengel61926280.42