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Showing a model's eye movements in examples does not improve learning of problem-solving tasks

机译:在示例中显示模型的眼动并不能改善对解决问题任务的学习

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Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention EMME have been shown to enhance learning of perceptual classification tasks; however, it is an open question whether EMME would also improve learning of procedural problem-solving tasks. We investigated this question in two experiments. In Experiment 1 (72 university students, M-age = 19.94), the effectiveness of EMME for learning simple geometry problems was addressed, in which the eye movements cued the underlying principle for calculating an angle. The only significant difference between the EMME and a no eye movement control condition was that participants in the EMME condition required less time for solving the transfer test problems. In Experiment 2 (68 university students, M-age = 21.12), we investigated the effectiveness of EMME for more complex geometry problems. Again, we found no significant effects on performance except for time spent on transfer test problems, although it was now in the opposite direction: participants who had studied EMME took longer to solve those items. These findings suggest that EMME may not be more effective than regular video examples for teaching procedural problem-solving skills. (C) 2016 Elsevier Ltd. All rights reserved.
机译:眼动建模示例(EMME)是人类模型(例如,老师)基于计算机的任务的演示,该模型的眼动叠加在任务上以指导学习者的注意力EMME已被证明可以增强感知分类的学习任务;但是,EMME是否还会改善对程序性问题解决任务的学习是一个悬而未决的问题。我们通过两个实验研究了这个问题。在实验1(72名大学生,M-年龄= 19.94)中,解决了EMME在学习简单几何问题方面的有效性,其中眼球运动暗示了计算角度的基本原理。 EMME和无眼球运动控制条件之间的唯一显着差异是,EMME条件下的参与者需要更少的时间来解决传递测试问题。在实验2(68名大学生,M-年龄= 21.12)中,我们研究了EMME对于更复杂的几何问题的有效性。再次,我们发现除了花在转移测试问题上的时间以外,对性能没有显着影响,尽管现在情况相反:研究EMME的参与者花了更长的时间来解决这些问题。这些发现表明,EMME可能无法比常规视频示例更有效地教授程序性问题解决技能。 (C)2016 Elsevier Ltd.保留所有权利。

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