article

Visual attention for solving multiple-choice science problem: An eye-tracking analysis

Authors:

Meng-Jung Tsai,

Fang-Ying YangAuthors Info & Claims

Computers & Education, Volume 58, Issue 1

Pages 375 - 385

https://doi.org/10.1016/j.compedu.2011.07.012

Published: 01 January 2012 Publication History

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) x 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.

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Published In

cover image Computers & Education

Computers & Education Volume 58, Issue 1

January, 2012

678 pages

ISSN:0360-1315

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Copyright © Elsevier Ltd © 2011.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 January 2012

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Cited By

Luo FLiu RAwoyemi ICrawford CNasrin FStephenson BStone JBattestilli LRebelsky SShoop L(2024)Novel Insights into Elementary Girls' Experiences in Physiological ComputingProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630900(764-770)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630900
Chen FCui YLutsyk-King AGao YLiu XCutumisu MLeighton J(2024)Validating a novel digital performance-based assessment of data literacy: Psychometric and eye-tracking analysesEducation and Information Technologies10.1007/s10639-023-12177-729:8(9417-9444)Online publication date: 1-Jun-2024
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Armut MKara M(2024)How do L2 learners of Turkish read texts with graphics? An eye-tracking studyEducation and Information Technologies10.1007/s10639-023-12017-829:4(4699-4743)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10639-023-12017-8
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