Eye movement modelling examples (EMME) is an emerging instructional design that effectively supports multimedia learning. It records the eye-movement tracks of experts in the process of learning or solving problems and superimposes them onto video materials to show novice or less experienced learners. Previous studies have confirmed the effectiveness of eye movement examples in attentional guidance and the promotion of multimedia learning from the perspective of attention and (meta)cognitive processing. Recent studies have shown that social information of gaze cues (e.g., learners’ social beliefs about gaze cues) may have an impact on the effectiveness of EMME. Through two experiments, this study investigated the impact of social cues and their associated social beliefs on the effectiveness of EMME. This study aims to enhance the application of eye movement examples and improve the outcomes of multimedia learning.
In Experiment 1, three groups of learners with different social beliefs about gaze cues were set up by manipulating the instruction: the expert model group (expert gaze cues - expert instruction), the peer model group (expert gaze cues - peer instruction), and the physical cues group (expert gaze cues - computer generated instruction). A control group without eye movement examples was also established to investigate the impact of learners’ social beliefs on the effectiveness of EMME. Building upon Experiment 1, Experiment 2 further investigated how different types of model gaze cues (e.g., peers or experts) and learners’ social beliefs about these cues (instruction gaze cues from an expert or peer) independently or jointly affect the EMME effect. The purpose of Experiment 2 was not only to validate the results of Experiment 1 but also to elucidate the role of social cues associated with gaze cues in the mechanism of EMME.
The results of Experiment 1 showed that compared with the control group, learners in the expert model group, peer model group, and physical cues group had a higher proportion of fixation in the interest area and a shorter time before the first fixation. Learning was enhanced by eye movement examples only when learners perceived the eye movement trajectory to be recorded by experts. These results indicate that gaze cues can provide a stable guide for attention, while learners’ social beliefs significantly affect learning outcomes. The results of experiment 2 showed that compared with peer gaze cues, learners under expert gaze cues had longer fixation time, shorter time before the first fixation, and higher retention and transfer scores. These results suggest that expert gaze cues effectively direct learners’ attention allocation and facilitate cognitive processing, thereby improving the learning outcomes. Additionally, learners under the instruction gaze cues from expert condition showed shorter initial fixation times and higher transfer test scores compared to those under the instruction gaze cues from peer condition, indicating that learners’ social beliefs about gaze cues significantly impact the EMME effect. Combined with the subjective questionnaire, it is found that learners believe that the expert eye movement track is more helpful, and their learning motivation is higher. This belief may prompt them to process the learning content more deeply.
This study indicates that both “looking with whom” and “thinking about looking with whom” impact the multimedia learning effect. Among them, the gaze cues of eye movement examples have a stable attention guidance effect, in which the expert gaze cues are especially helpful in guiding attention and promoting cognitive processing. Additionally, the learners’ social beliefs about gaze cues affect the learning outcomes. When learners believe that eye movement tracks are recorded by experts, they show better academic performance, which may be related to their expectations of gaze cues and their learning motivation. This study reveals the important role of expert gaze cues and their associated social beliefs in optimizing learning results in EMME, providing guidance for the optimization and application of eye movement examples.