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Using eye movement modelling examples to guide visual attention and foster cognitive performance: A meta-analysis
Journal of Computer Assisted Learning ( IF 3.761 ) Pub Date : 2021-05-31 , DOI: 10.1111/jcal.12568 Heping Xie 1 , Tingting Zhao 2, 3 , Sue Deng 4 , Ji Peng 2, 3 , Fuxing Wang 2, 3 , Zongkui Zhou 2, 3
Journal of Computer Assisted Learning ( IF 3.761 ) Pub Date : 2021-05-31 , DOI: 10.1111/jcal.12568 Heping Xie 1 , Tingting Zhao 2, 3 , Sue Deng 4 , Ji Peng 2, 3 , Fuxing Wang 2, 3 , Zongkui Zhou 2, 3
Affiliation
Eye movement modelling examples (EMME) are computer-based videos displaying the visualized eye gaze behaviour of a domain expert person (model) while carefully executing the learning or problem-solving task. The role of EMME in promoting cognitive performance (i.e., final scores of learning outcome or problem solving) has been questioned due to the mixed findings from empirical studies. This study tested the effects of EMME on attention guidance and cognitive performance by means of meta-analytic procedures. Data for both experimental and control groups and both posttest and pretest were extracted to calculate the effect sizes. The EMME group was treated as the experimental group and the non-EMME group was treated as the control group. Twenty-five independent articles were included. The overall analysis showed a significant effect of EMME on time to first fixation (d = −0.83), fixation duration (d = 0.74), as well as cognitive performance (d = 0.43), but not on fixation count, indicating that using EMME not only helped learners attend faster and longer to the task-relevant elements, but also fostered their final cognitive performance. Interestingly, task type significantly moderated the effect of EMME on cognitive performance. Moderation analyses showed that EMME was beneficial to learners' performance when non-procedural tasks (rather than procedural tasks) were used. These findings show contributions for future research as well as practical application in the field of computers and learning regarding videos displaying a model's visualized eye gaze behaviour.
中文翻译:
使用眼动建模示例来引导视觉注意力和培养认知能力:荟萃分析
眼动建模示例 (EMME) 是基于计算机的视频,显示领域专家(模型)在仔细执行学习或解决问题的任务时的可视化眼睛注视行为。由于实证研究的不同结果,EMME 在促进认知表现(即学习成果或问题解决的最终分数)方面的作用受到质疑。本研究通过元分析程序测试了 EMME 对注意力引导和认知表现的影响。提取实验组和对照组以及后测和前测的数据以计算效果大小。EMME组为实验组,非EMME组为对照组。包括 25 篇独立文章。d = -0.83)、注视持续时间 ( d = 0.74) 以及认知表现 ( d = 0.43),但不涉及注视计数,这表明使用 EMME 不仅有助于学习者更快、更长时间地参与与任务相关的元素,但也培养了他们最终的认知表现。有趣的是,任务类型显着缓和了 EMME 对认知表现的影响。适度分析表明,当使用非程序性任务(而不是程序性任务)时,EMME 有利于学习者的表现。这些发现显示了对未来研究以及计算机领域的实际应用的贡献,以及关于显示模型可视化眼睛注视行为的视频的学习。
更新日期:2021-07-09
中文翻译:
使用眼动建模示例来引导视觉注意力和培养认知能力:荟萃分析
眼动建模示例 (EMME) 是基于计算机的视频,显示领域专家(模型)在仔细执行学习或解决问题的任务时的可视化眼睛注视行为。由于实证研究的不同结果,EMME 在促进认知表现(即学习成果或问题解决的最终分数)方面的作用受到质疑。本研究通过元分析程序测试了 EMME 对注意力引导和认知表现的影响。提取实验组和对照组以及后测和前测的数据以计算效果大小。EMME组为实验组,非EMME组为对照组。包括 25 篇独立文章。d = -0.83)、注视持续时间 ( d = 0.74) 以及认知表现 ( d = 0.43),但不涉及注视计数,这表明使用 EMME 不仅有助于学习者更快、更长时间地参与与任务相关的元素,但也培养了他们最终的认知表现。有趣的是,任务类型显着缓和了 EMME 对认知表现的影响。适度分析表明,当使用非程序性任务(而不是程序性任务)时,EMME 有利于学习者的表现。这些发现显示了对未来研究以及计算机领域的实际应用的贡献,以及关于显示模型可视化眼睛注视行为的视频的学习。