Abstract
Eye-tracking technology has been widely used in educational research to access students’ learning processes. However, analyzing and comprehending students’ eye movements is a big challenge as eye movement data is enormous and complex. This paper attempts to develop a visualization system presenting students’ eye movements to educational researchers to understand students’ problem-solving processes. More specifically, the visualization system is developed to illustrate how the visualization method can present students’ eye movement data for educational researchers to achieve insights and make hypotheses about students’ problem-solving strategies. Elementary school students’ problem-solving data, including performance and eye movement data, were collected and visualized. Two educational researchers and one visualization designer were recruited to evaluate the visualization system and compare it to the traditional e-learning analysis method – video recordings. The evaluation results show that the visualization is easy to understand and can help evaluators to identify students’ attention patterns and problem-solving strategies quickly. However, the visualization system provided less information than video recordings, e.g., problem-solving context and mouse movement. Our work shows a promising future of using visualization to help researchers and teachers to provide targeted intervention to help young students learn the correct strategy of math problem-solving.
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Wei, S., Xin, Y.P., Chen, Y. (2020). Visualizing Students’ Eye Movement Data to Understand Their Math Problem-Solving Processes. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. Human and Technology Ecosystems. HCII 2020. Lecture Notes in Computer Science(), vol 12206. Springer, Cham. https://doi.org/10.1007/978-3-030-50506-6_15
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