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How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System?

  • Michelle TaubEmail author
  • Roger Azevedo
Article

Abstract

The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.

Keywords

Intelligent tutoring systems Metacognition Multichannel data Prior knowledge Self-regulated learning 

Notes

Acknowledgments

This research was supported by funding from the National Science Foundation (DRL#1431552; DRL#1660878, DRL#1661202) and the Social Sciences and Humanities Research Council of Canada (SSHRC 895-2011-1006). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Social Sciences and Humanities Research Council of Canada. The authors would like to thank the members from the SMART Lab at NCSU for their assistance with data collection.

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Copyright information

© International Artificial Intelligence in Education Society 2018

Authors and Affiliations

  1. 1.Department of PsychologyNorth Carolina State UniversityRaleighUSA

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