Modeling metacomprehension monitoring accuracy with eye gaze on informational content in a multimedia learning environment

Abstract

Multimedia learning environments support learners in developing self-regulated learning (SRL) strategies. However, capturing these strategies and cognitive processes can be difficult for researchers because cognition is often inferred, not directly measured. This study sought to model self-reported metacognitive judgments using eye-tracking from 60 undergraduate students as they learned about biological systems with MetaTutorIVH, a multimedia learning environment. We found that participants' gaze behaviors were different between the perceived relevance of the instructional content provided regardless of the actual content relevance. Additionally, we fit a cumulative link mixed effects ordinal regression model to explain reported metacognitive judgments based on content fixations, relevance, and presentation type. Main effects were found for all variables and several interactions between both fixations and content relevance as well as content fixations and presentation type. Surprisingly, accurate metacognitive judgments did not explain performance. Implication for multimedia learning environment design are discussed.