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2018 | OriginalPaper | Chapter

How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems?

Authors : Michelle Taub, Roger Azevedo, Nicholas V. Mudrick

Published in: Intelligent Tutoring Systems

Publisher: Springer International Publishing

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Abstract

We investigated how college students’ (n = 40) different levels of action unit 4 (AU4: brow lowerer), metacognitive monitoring process use and pre-test score were associated with metacognitive monitoring accuracy during learning with a hypermedia-based ITS. Results revealed that participants with high pre-test scores had the highest accuracy scores with low levels of AU4 and use of more metacognitive monitoring processes, whereas participants with low pre-test scores had higher accuracy scores with high levels of AU4 and use of more metacognitive monitoring processes. Implications include designing adaptive ITSs that provide different types of scaffolding based on levels of prior knowledge, use of metacognitive monitoring processes, and emotional expressivity keeping in mind that levels of emotions change over time, and therefore must be monitored to provide effective scaffolding during learning.

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previous chapter A Heuristic Approach for New-Item Cold Start Problem in Recommendation of Micro Open Education Resources

next chapter How Are Students’ Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs?

This is a subset of a sample of 62 participants, as we did not include participants who did not have facial expression data.

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Taub, M., Mudrick, N.V., Azevedo, R., Millar, G.C., Rowe, J., Lester, J.: Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island. Comput. Hum. Behav. 76, 641–655 (2017)CrossRef

D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., Perkins, L., Graesser, A.: A time for emoting: when affect-sensitivity is and isn’t effective at promoting deep learning. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 245–254. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13388-6_29CrossRef

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Grafsgaard, J.F., Wiggins, J.B., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Automatically recognizing facial expression: predicting engagement and frustration. In: 6th International Conference Educational Data Mining, EDM 2013, pp. 43–50 (2013)

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Title: How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems?
Authors: Michelle Taub
Roger Azevedo
Nicholas V. Mudrick
Publisher: Springer International Publishing
Book: Intelligent Tutoring Systems
Print ISBN: 978-3-319-91463-3

Electronic ISBN: 978-3-319-91464-0

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91464-0_22

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