Leveraging College Students’ Scientific Evidence-Based Reasoning Performance with Eye-Tracking-Supported Metacognition

Pei Yi Tsai, Ting Ting Yang, Hsiao Ching She, Sheng Chang Chen

研究成果: Article

摘要

This study specifically focuses on examining whether the eye-tracking-supported metacognition would benefit science majors’ and nonscience majors’ scientific evidence-based reasoning performance. Thirty-nine science majors and forty-one nonscience majors were recruited to participate in an online scientific evidence-based reasoning task. Data regarding the students’ online learning process and eye movement behaviors were simultaneously collected. The results indicated that the science majors not only significantly outperformed the nonscience majors in terms of reasoning performance but also allocated significantly greater eye movements during their first time of processing scientific evidence-based reasoning task. Immediately after the task, the eye-tracking-supported metacognition provided each student with individualized feedback regarding their eye movement behaviors, such as their eye fixation sequence, durations, and locations. With such immediate feedback, the students were provided an opportunity to engage in self-monitoring, evaluating, and calibrating their approaches in order to revise their final answers. After the application of this eye-tracking-supported metacognition, both the science majors and the nonscience majors all made significant improvements in their scientific evidence-based reasoning performance. However, no statistically significant differences in the reasoning performance or visual attention of the science majors and nonscience majors were found. This study demonstrated that the use of eye-tracking-supported metacognition was not only able to maximize the performance of both the science majors and nonscience majors but that it also bridged the gap in performance between the two groups.

原文English
頁(從 - 到)613-627
頁數15
期刊Journal of Science Education and Technology
28
發行號6
DOIs
出版狀態Published - 2019 十二月 1

All Science Journal Classification (ASJC) codes

  • Education
  • Engineering(all)

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