Using Eye-Tracking to Determine the Impact of Prior Knowledge on Self-Regulated Learning with an Adaptive Hypermedia-Learning Environment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)


Recent research on self-regulated learning (SRL) includes multi-channel data, such as eye-tracking, to measure the deployment of key cognitive and metacognitive SRL processes during learning with adaptive hypermedia systems. In this study we investigated how 147 college students’ proportional learning gains (PLGs), proportion of time spent on areas of interest (AOIs), and frequency of fixations on AOI-pairs, differed based on their prior knowledge of the overall science content, and of specific content related to sub-goals, as they learned with MetaTutor. Results indicated that students with low prior sub-goal knowledge had significantly higher PLGs, and spent a significantly larger proportion of time fixating on diagrams compared to students with high prior sub-goal knowledge. In addition, students with low prior knowledge had significantly higher frequencies of fixations on some AOI-pairs, compared to students with high prior knowledge. The results have implications for using eye-tracking (and other process data) to understand the behavioral patterns associated with underlying cognitive and metacognitive SRL processes and provide real-time adaptive instruction, to ensure effective learning.


Metacognition Self-regulated learning Eye tracking Prior knowledge Adaptive hypermedia-learning environments Process data 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Psychology, Laboratory for the Study of Metacognition and Advanced Learning TechnologiesNorth Carolina State UniversityRaleighUSA

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