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 modelling exam...
| Publicado en: | Journal of Computer Assisted Learning Vol. 36; no. 4; pp. 569 - 580 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144562048&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144562048 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2020 vid: 36 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 144562048 144562048 144562048 10.1111/jcal.12428 144562048 ppf: 569 ppct: 11 formats: tig: atl: Effects of prior knowledge and joint attention on learning from eye movement modelling examples. aug: au: Chisari, Lucia B. Mockevičiūtė, Akvilė Ruitenburg, Sterre K. Vemde, Lian Kok, Ellen M. Gog, Tamara affil: Department of Education, Utrecht University, , The Netherlands sug: subj: Eye Movements Learning Methods Attention Knowledge Audiovisuals Outcomes of Education Human Videorecording Eye Movement Measurements Structural Equation Modeling Funding Source Students, College Netherlands Adult Male Female Descriptive Statistics Analysis of Variance Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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