How Are Students’ Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs?
The goal of this study was to examine 38 undergraduate and graduate students’ note taking and summarizing, and the relationship between emotions, the accuracy of those notes and summaries, and proportional learning gain, during learning with MetaTutor, an ITS that fosters self-regulated learning while learning complex science topics. Results revealed that students expressed both positive (i.e., joy, surprise) and negative (i.e., confusion, frustration, anger, and contempt) emotions during note taking and summarizing, and that these emotions correlated with each other, as well as with proportional learning gain and accuracy of their notes and summaries. Specifically, contempt during note taking was positively correlated with proportional learning gain; note taking accuracy was negatively correlated with proportional learning gain; and confusion during summarizing was positively correlated with summary accuracy. These results reveal the importance of investigating specific self-regulated learning processes, such as taking notes or making summaries, with future research aimed at examining the differences and similarities between different cognitive and metacognitive processes and how they interact with different emotions similarly or differently during learning. Implications of these findings move us toward developing adaptive ITSs that foster self-regulated science learning, with specific scaffolding based on each individual student’s learning needs.
KeywordsCognitive learning strategies Facial expressions of emotion Latent semantic analysis Process data Self-regulated learning
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). The authors would like to thank the members from the SMART Lab at NCSU for their assistance with data collection.
- 1.Azevedo, R., Taub, M., Mudrick, N. V.: Understanding and reasoning about real-time cognitive, affective, and metacognitive processes to foster self-regulation with advanced learning technologies. In: Alexander, P.A., Schunk, D.H., and Greene, J.A. (eds.) Handbook of Self-regulation of Learning and Performance, 2nd ed., pp. 254–270. Routledge, New York (2018)Google Scholar
- 3.Winne, P.H.: Cogniion and metacognition within self-regulated learning. In: Alexander, P.A., Schunk, D.H., Greene, J.A. (eds.) Handbook of Self-regulation of Learning and Performance, 2nd ed., pp. 36–48. Routledge, New York (2018)Google Scholar
- 5.D’Mello, S., Graesser, A.C.: Feeling, thinking, and computing with affect-aware learning technologies. In: Calvo, R.A., D’Mello, S.K., Gratch, J., Kappas, A. (eds.) Handbook of Affective Computing, pp. 419–434. Oxford University Press, New York (2015)Google Scholar
- 7.Witherspoon, A.M., Azevedo, R., D’Mello, S.: The dynamics of self-regulatory processes within self-and externally regulated learning episodes during complex science learning with hypermedia. In: Woolf, Beverley P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 260–269. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69132-7_30CrossRefGoogle Scholar
- 8.Landauer, T., McNamara, D.S., Dennis, S., Kintsch, W.: Handbook of Latent Semantic Analysis. Erlbaum, Mahwah (2007)Google Scholar
- 9.Dente, P., Küster, D., Skora, L., Krumhuber, E.G.: Measures and metrics for automatic emotion classification via FACET. In: Bryson, J., De Vos, M., and Padget, J. (eds.) Proceedings of the Conference on the Study of Artificial Intelligence and Simulation of Behaviour (AISB), pp. 160–163 (2017)Google Scholar
- 10.Ekman, P., Friesen, W.V., Hager, J.C.: Facial Action Coding System. Network Information Research Corporation, Salt Lake City (2002)Google Scholar
- 11.Littlewort, G., Wu, T., Whitehill, J., Fasel, I., Movellan, J., Bartlett, M.: CERT computer expression recognition tool. In: Automatic Face and Gesture Recognition, pp. 298–305. IEEE, New York (2011)Google Scholar
- 14.Azevedo, R., Taub, M., Mudrick, N.V., Millar, G.C., Bradbury, A.E., Price, M.J.: Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies. In: Buder, J., Hesse, F.W. (eds.) Informational Environments: Effects of Use, Effective Designs, pp. 225–247. Springer, Amsterdam, The Netherlands (2017). https://doi.org/10.1007/978-3-319-64274-1_10CrossRefGoogle Scholar