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

Abstract

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.

A review of eye tracking research on video-based learning

“…For example, empirical research has shown that the instructor’s image facilitates the learning of declarative knowledge but interferes with procedural knowledge (Hong et al, 2018 ). A recent meta-analysis has also shown that visual cues were most beneficial to learning performance when non-procedural tasks were taught in instructional videos (Xie et al, 2021 ). Practitioners need this contextual information to determine the boundary conditions for video design principles to be effective.…”

Re‐viewing performance: Showing eye‐tracking data as feedback to improve performance monitoring in a complex visual task

“…Such visualizations, also known as gaze displays (see Figure 1) have been suggested to provide a promising tool for education (Jarodzka et al, 2017; van Gog, Kester, et al, 2009; Van & Scheiter, 2010). For instance, it has been shown that displaying the gaze of a teacher to learners in eye movement modelling examples enhances learning on a variety of tasks compared to regular video modelling examples without the gaze displayed (e.g., Chisari et al, 2020; Jarodzka et al, 2012; Mason et al, 2015; Van Gog et al, 2009; for a meta‐analysis see Xie et al, 2021). It has also been proposed that displaying the gaze of a learner back to them as a cue could improve monitoring accuracy (van Gog et al, 2009).…”

Applying Eye-Tracking Technology in the Field of Entrepreneurship Education

“…EMMEs are computer-based videos that display the gaze behaviour of a domain expert while they execute a problem-solving task. This is a training model, where (relative) novices learn from the experts using EMME as an instructional document (Xie et al, 2021). EMME video examples are professional vision (or the layover of the experts' visual focuses) simultaneously supplemented by the verbal explanations of the expert for complex real-life problems to be dissected and explained following the cognitive processes of the expert.…”