Visualizing a Task Performer’s Gaze to Foster Observers’ Performance and Learning : a Systematic Literature Review on Eye Movement Modeling Examples

Eye movement modeling examples (EMMEs) are instructional videos (e.g., tutorials) that visualize another person’s gaze location while they demonstrate how to perform a task. This systematic literature review provides a detailed overview of studies on the effects of EMME to foster observers’ performance and learning and highlights their differences in EMME designs. Through a broad, systematic search on four relevant databases, we identified 72 EMME studies (78 experiments). First, we created an overview of the different study backgrounds. Studies most often taught tasks from the domains of sports/physical education, medicine, aviation, and STEM areas and had different rationales for displaying EMME. Next, we outlined how studies differed in terms of participant characteristics, task types, and the design of the EMME materials, which makes it hard to infer how these differences affect performance and learning. Third, we concluded that the vast majority of the experiments showed at least some positive effects of EMME during learning, on tests directly after learning, and tests after a delay. Finally, our results provide a first indication of which EMME characteristics may positively influence learning. Future research should start to more systematically examine the effects of specific EMME design choices for specific participant populations and task types

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Looking Through the Teacher’s Eyes: Effects of Eye Movement Modeling Examples on Learning to Solve Procedural Problems

Learning by watching the good example of others is a very natural and effective way of learning. Nowadays, we can view examples of experts from over the whole world by means of video examples. This dissertation describes research about a new type of video examples, so-called eye movement modeling examples (EMME), in which learners not only see what a teacher is doing on the computer but also see where he or she looks at. This is done by recording the teacher’s eye movements and depicting them as a moving colored circle or dot. Showing where the teacher looks serves two purposes: It guides the learner’s attention towards the right place at the right time which makes the explanation of the teacher easier to understand, and it provides insight into which information the teacher uses to perform the task and which strategy he or she uses (something which usually remains invisible). Previous research showed that EMME (compared with regular video examples without the depiction of the teacher’s eye movements) improved the performance on visual search tasks and classification tasks. In his dissertation Tim van Marlen examined whether EMME can also improve learning to solve procedural problems, such as geometry problems. His research showed that EMME indeed help to guide the learner’s attention towards the right place at the right time. In addition, secondary education students performed better at solving geometry problems after studying EMME than students studying regular video examples

Looking Through the Teacher’s Eyes: Effects of Eye Movement Modeling Examples on Learning to Solve Procedural Problems

Learning by watching the good example of others is a very natural and effective way of learning. Nowadays, we can view examples of experts from over the whole world by means of video examples. This dissertation describes research about a new type of video examples, so-called eye movement modeling examples (EMME), in which learners not only see what a teacher is doing on the computer but also see where he or she looks at. This is done by recording the teacher’s eye movements and depicting them as a moving colored circle or dot. Showing where the teacher looks serves two purposes: It guides the learner’s attention towards the right place at the right time which makes the explanation of the teacher easier to understand, and it provides insight into which information the teacher uses to perform the task and which strategy he or she uses (something which usually remains invisible). Previous research showed that EMME (compared with regular video examples without the depiction of the teacher’s eye movements) improved the performance on visual search tasks and classification tasks. In his dissertation Tim van Marlen examined whether EMME can also improve learning to solve procedural problems, such as geometry problems. His research showed that EMME indeed help to guide the learner’s attention towards the right place at the right time. In addition, secondary education students performed better at solving geometry problems after studying EMME than students studying regular video examples

Effects of visual complexity and ambiguity of verbal instructions on target identification

Research has shown that visual complexity and the ambiguity of verbal information affect the speed and accuracy of locating targets during visual search. The higher the visual complexity and description ambiguity, the slower and poorer the target identification performance. Because these factors are seldom studied in combination (even though they regularly co-occur), it is unclear whether they would interact. Therefore, in two experiments, participants viewed images that displayed cartoon-like characters and had to correctly identify a character from a verbal description under conditions of low/high visual complexity and low/high description ambiguity (manipulated within-subjects). Results revealed that high ambiguity descriptions resulted in lower accuracy and slower response times. However, our manipulation of visual complexity did not affect performance or response times either in itself or in interaction with verbal ambiguity. Findings are discussed in terms of theoretical and practical implications, for instance, for multimedia learning

Showing a model's eye movements in examples does not improve learning of problem-solving tasks

Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention. EMME have been shown to enhance learning of perceptual classification tasks; however, it is an open question whether EMME would also improve learning of procedural problem-solving tasks. We investigated this question in two experiments. In Experiment 1 (72 university students, Mage = 19.94), the effectiveness of EMME for learning simple geometry problems was addressed, in which the eye movements cued the underlying principle for calculating an angle. The only significant difference between the EMME and a no eye movement control condition was that participants in the EMME condition required less time for solving the transfer test problems. In Experiment 2 (68 university students, Mage = 21.12), we investigated the effectiveness of EMME for more complex geometry problems. Again, we found no significant effects on performance except for time spent on transfer test problems, although it was now in the opposite direction: participants who had studied EMME took longer to solve those items. These findings suggest that EMME may not be more effective than regular video examples for teaching procedural problem-solving skills

Effectiveness of eye movement modeling examples in problem solving : The role of verbal ambiguity and prior knowledge

Eye movement modeling examples (EMME) are video modeling examples with the model's eye movements superimposed. Thus far, EMME on problem-solving tasks seem to be effective for guiding students’ attention, but this does not translate into higher learning outcomes. We therefore investigated the role of ambiguity of the verbal explanation and prior knowledge in the effectiveness of EMME on geometry problems. In Experiment 1, 57 university students observed EMME or regular video modeling examples (ME) with ambiguous verbal explanations. Eye-tracking data revealed that –as in prior research with unambiguous explanations- EMME successfully guided students’ attention but did not improve test performance, possibly due to students’ high prior knowledge. Therefore, Experiment 2, was conducted with 108 secondary education students who had less prior knowledge, using a 2 (EMME/ME) x 2 (ambiguous/unambiguous explanations) between-subjects design. Verbal ambiguity did not affect learning, but students in the EMME conditions outperformed those in the ME conditions

Showing a model's eye movements in examples does not improve learning of problem-solving tasks

Eye movement modeling examples (EMME) are demonstrations of a computer-based task by a human model (e.g., a teacher), with the model's eye movements superimposed on the task to guide learners' attention. EMME have been shown to enhance learning of perceptual classification tasks; however, it is an open question whether EMME would also improve learning of procedural problem-solving tasks. We investigated this question in two experiments. In Experiment 1 (72 university students, Mage = 19.94), the effectiveness of EMME for learning simple geometry problems was addressed, in which the eye movements cued the underlying principle for calculating an angle. The only significant difference between the EMME and a no eye movement control condition was that participants in the EMME condition required less time for solving the transfer test problems. In Experiment 2 (68 university students, Mage = 21.12), we investigated the effectiveness of EMME for more complex geometry problems. Again, we found no significant effects on performance except for time spent on transfer test problems, although it was now in the opposite direction: participants who had studied EMME took longer to solve those items. These findings suggest that EMME may not be more effective than regular video examples for teaching procedural problem-solving skills

Looking through Sherlock's eyes: Effects of eye movement modelling examples with and without verbal explanations on deductive reasoning

Background: Eye movement modelling examples (EMME) are demonstrations in which learners' not only see a model's (e.g., a teacher's) task performance on a computer screen (as in regular video examples) but also the model's eye movements (represented as moving coloured dots overlaid on the screen). Thereby EMME help guide learners' attention towards the relevant information and can model cognitive strategies which are otherwise unobservable for learners. Objectives: This study investigated whether EMME can help to learn deductive reasoning strategies and how the presence/absence of a teacher's verbal explanation affects learning from EMME. Methods: Secondary education students (N = 137) were randomly assigned to study video examples under one of four conditions in a 2 (EMME: yes/no) x 2 (verbal explanations: yes/no) between-subjects design. Results and Conclusions: Results revealed only a beneficial effect of the presence of verbal explanations on performance on the practice problems, but no pretest-to-posttest learning gains. Implications: Seeing the teacher's eye movements does not appear to enhance learning of deductive reasoning. The presence/absence of the teacher's verbal explanation does not seem to affect learning deductive reasoning