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Effects of prior knowledge and joint attention on learning from eye movement modelling examples

机译:先前知识与关注对眼运动建模示例学习的影响

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Abstract Eye movement modelling examples (EMMEs) are instructional videos of a model's demonstration and explanation of a task that also show where the model is looking. EMMEs are expected to synchronize students' visual attention with the model's, leading to better learning than regular video modelling examples (MEs). However, synchronization is seldom directly tested. Moreover, recent research suggests that EMMEs might be more effective than ME for low prior knowledge learners. We therefore used a 2 × 2 between‐subjects design to investigate if the effectiveness of EMMEs (EMMEs/ME) is moderated by prior knowledge (high/low, manipulated by pretraining), applying eye tracking to assess synchronization. Contrary to expectations, EMMEs did not lead to higher learning outcomes than ME, and no interaction with prior knowledge was found. Structural equation modelling shows the mechanism through which EMMEs affect learning: Seeing the model's eye movements helped learners to look faster at referenced information, which was associated with higher learning outcomes.
机译:抽象的眼球运动建模示例(EMME)是模型的演示和解释的教学视频,该任务也显示了模型正在寻找的位置。预计EMME将与模型同步学生的视觉关注,导致比常规视频建模示例(MES)更好地学习。但是,同步很少测试。此外,最近的研究表明EMME可能比我更有效地对低先生知识学习者更有效。因此,我们在主题设计中使用了2×2的主题设计来调查EMMES(EMMES / ME)的有效性是否通过先前的知识(高/低,通过预先预订)来调查,应用眼睛跟踪来评估同步。与期望相反,艾米人没有导致高等学校的结果,而不是发现与先前知识的互动。结构方程模型显示EMMES影响学习的机制:看到模型的眼球运动有助于学习者在引用的信息时看起来更快,这与高等学习结果相关。

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