Abstract
Students need to accurately monitor and judge the difficulty of learning materials to effectively self-regulate their learning with advanced learning technologies such as intelligent tutoring systems (ITSs), including MetaTutorIVH. However, there is a paucity of research examining how metacognitive monitoring processes such as ease of learning (EOLs) judgments can be used to provide adaptive scaffolding and predict student performance during learning ITSs. In this paper, we report on a study investigating how students’ EOL judgments can influence their performance and significantly predict their learning outcomes during learning with MetaTutorIVH, an ITS for human physiology. The results have important design implications for incorporating different types of metacognitive judgements in student models to support metacognition and foster learning of complex ITSs.
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Acknowledgements
This research was supported by funding from the National Science Foundation (DRL#1431552). The authors would also like to thank members from the SMART Lab and IntelliMedia Group for their contributions to this project.
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Mudrick, N.V., Sawyer, R., Price, M.J., Lester, J., Roberts, C., Azevedo, R. (2018). Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_14
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DOI: https://doi.org/10.1007/978-3-319-91464-0_14
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