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

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...

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Veröffentlicht in:Journal of computer assisted learning 2020-08, Vol.36 (4), p.569-579
Hauptverfasser: Chisari, Lucia B., Mockevičiūtė, Akvilė, Ruitenburg, Sterre K., Vemde, Lian, Kok, Ellen M., Gog, Tamara
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Sprache:eng
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Zusammenfassung: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. Lay Description What is already known about this topic Modelling, which involves an expert model showing learners the completion of a task, is an important form of teaching. Learning from modelling examples can be enhanced by showing eye movements of the model, so‐called eye movement modelling examples (EMMEs). Results regarding the effectiveness of EMMEs are mixed, which could be caused by differences in learners' prior knowledge. Eye‐tracking technology can be used to investigate how EMMEs enhance learning from modelling examples. What this paper adds EMMEs effectively guide attention: Students follow the models' gaze better and look faster at relevant information. No differences in learning were found between EMMEs and a modelling example without eye movements. We manipulated prior knowledge but found that it did not moderate the effectiveness of EMMEs. The working mechanism of EMMEs seems to be that it helps learners to attend to relevant information faster. Implications for practice and/or policy A video of a model's gaze can help learners attend to relevant information. Such videos are particularly useful if learners need to quickly look at relevant information (e.g., in learning from animations). For some tasks, EMMEs and regular modelling examples are equally effective. Training task‐specific picture–word correspondences before presenting a modelling example supports learning.
ISSN:0266-4909
1365-2729
DOI:10.1111/jcal.12428