Abstract

This study employed an eye-tracking technique to examine students’ visual attention when solving a multiple-choice science problem. Six university students participated in a problem-solving task to predict occurrences of landslide hazards from four images representing four combinations of four factors. Participants’ responses and visual attention were recorded by an eye tracker. Participants were asked to think aloud during the entire task. A 4 (options) × 4 (factors) repeated measures design, two paired t-tests and effect sizes analyses were conducted to compare the fixation duration between chosen and rejected options and between relevant and irrelevant factors. Content analyses were performed to analyze participants’ responses and think aloud protocols and to examine individual’s Hot Zone image. Finally, sequential analysis on fixated LookZones was further utilized to compare the scan patterns between successful and unsuccessful problem solvers. The results showed that, while solving an image-based multiple-choice science problem, students, in general, paid more attention to chosen options than rejected alternatives, and spent more time inspecting relevant factors than irrelevant ones. Additionally, successful problem solvers focused more on relevant factors, while unsuccessful problem solvers experienced difficulties in decoding the problem, in recognizing the relevant factors, and in self-regulating of concentration. Future study can be done to examine the reliability and the usability of providing adaptive instructional scaffoldings for problem solving according to students’ visual attention allocations and transformations in a larger scale. Eye-tracking techniques are suggested to be used to deeply explore the cognitive process during e-learning and be applied to future online assessment systems.

Citation

Tsai, M.J., Hou, H.T., Lai, M.L., Liu, W.Y. & Yang, F.Y. (2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58(1), 375-385. Elsevier Ltd. Retrieved June 4, 2023 from https://www.learntechlib.org/p/50672/.

Keywords

• Applications in subject areas
• Attention
• Cognitive Processes
• College Students
• content analysis
• Effect Size
• electronic learning
• Evaluation methodology
• Eye Movements
• higher education
• Human Body
• Interdisciplinary Projects
• media in education
• Multiple Choice Tests
• problem solving
• Protocol Analysis
• science education
• Science Instruction
• Teaching/Learning Strategies