Using eye movement modelling examples to guide visual attention and foster cognitive performance: A meta‐analysis.

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...

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Detalles Bibliográficos
Publicado en:Journal of Computer Assisted Learning Vol. 37; no. 4; pp. 1194 - 1207
Autores principales: Xie, Heping, Zhao, Tingting, Deng, Sue, Peng, Ji, Wang, Fuxing, Zhou, Zongkui
Formato: meta analysis pictorial research tables/charts Journal Article
Publicado: Wiley-Blackwell Aug2021
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario: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. Lay Description: What is already known about this topic: How eye movements affect cognitive processes has attracted a lot of attention and it is regarded as an important question for the promotion of cognitive performance.Eye movement modelling examples (EMME) show the potential to guide visual attention and foster cognitive performance.The existing empirical findings of the effects of EMME are not consistent, especially regarding cognitive performance. What this paper adds: Overall, EMME can help learners attend faster and longer to the task‐relevant area.Overall, cognitive performance can be facilitated by EMME.Task type is a boundary condition for the role of EMME in the promotion of cognitive performance. Implications for practice and/or policy: Cognitive trainers, teachers, or other practitioners should carefully consider embedding expert models' eye movements into training or learning materials.When using EMME, practitioners should take the task type into consideration.