Abstract
Interactions based on the learners’ state of understanding and their attitudes toward tasks are considered important for realising a support system for collaborative learning. In this study, as a first step, we tried to detect whether the learner’s state is Passive in the ICAP theory from the data obtained during collaborative learning. We actually conducted an experiment of collaborative learning between participants and obtained data on facial features, gaze directions, and speech state during the experiment. Based on these data, we investigated clues to classify the status of ICAP as either Passive or not. As a result, we were able to find several candidates. On the other hand, in the state classification of participants’ states using these independent variables, it was not possible to show high accuracy. In future experiments, we plan to simultaneously measure physiological indices as a clue to estimate participants’ internal state.
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References
Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons learned. J. Learn. Sci. 4(2), 167–207 (1995)
Baltrušaitis, T., Robinson, P., Morency, L.: OpenFace: an open source facial behavior analysis toolkit. In: 2016 IEEE Winter Conference on Applications of Computer Vision, pp. 1–10 (2016)
Chi, M.T.H., Leeuw, N., Chiu, M., Lavancher, C.: Eliciting self-explanations improves understanding. Cogn. Sci. 18(3), 439–477 (1994)
Chi, M.T., Wylie, R.: The ICAP framework: linking cognitive engagement to active learning outcomes. Educ. Psychol. 49(4), 219–243 (2014)
Dillenbourg, P., Fischer, F.: Computer-supported collaborative learning: the basics. Zeitschrift für Berufs- und Wirtschaftspädagogik 21, 111–130 (2007)
Hayashi, Y.: Towards supporting collaborative learning with an intelligent tutoring system: predicting learning process by using gaze and verbal information. Cogn. Stud. 26(3), 343–356 (2019)
Misu, T., et al.: Modeling spoken decision support dialogue and optimization of its dialogue strategy. ACM Trans. Speech Lang. Process. (TSLP) 7(3), 10. 221–224 (2011)
Okada, T., Simon, H.A.: Collaborative discovery in a scientific domain. Cogn. Sci. 21(2), 109–146 (1997)
Raux, A., Langner, B., Bohus, D., Black, A.W., Eskenazi, M.: Let’s go public! taking a spoken dialog system to the real world. In: Ninth European Conference on Speech Communication and Technology (2005)
Rummel, N., Weinberger, A., Wecker, C., Fischer, F., Meier, A., Voyiatzaki, E., et al.: New challenges in CSCL: Towards adaptive script support. In: Kanselaar, G., Jonker, V., Kirschner, P. A., Prins, F.J. (eds.) Proceedings of ICLS 2008, pp. 338–345. International Society of the Learning Sciences, Utrecht (2008)
Shirouzu, H., Miyake, N., Masukawa, H.: Cognitively active externalization for situated reflection. Cogn. Sci. 26(4), 469–501 (2002)
Shimojo, S., Hayashi, Y.: An experimental investigation on collaborative dyads’ explanation activities using conceptual maps: analysis on learning performance based on understanding and the use of different perspectives. IEICE Tech. Rep. 119(39), 87–91 (2019a)
Shimojo, S., Hayashi, Y.: Relation between dialog activity and learning performance on collaborative learning visualized other knowledge: An analysis of turn-taking and knowledge convergence. Japanese Cogn. Sci. Soc. 36, 2–46 (2019b)
Shimojo, S., Hayashi, Y.: How shared concept mapping facilitates explanation activities in collaborative learning: an experimental investigation into learning performance in the context of different perspectives. In: Proceedings of the 27th International Conference on Computers in Education(ICCE2019), pp. 172–177 (2019c)
Weinberger, A., Fischer, F.: A framework to analyze argumentative knowledge construction in computer supported collaborative learning. Comput. Educ. 46(1), 71–95 (2006)
Wiggins, B.L., Eddy, S.L., Grunspan, D.Z., Crowe, A.J.: The ICAP active learning framework predicts the learning gains observed in intensely active classroom experiences. AERA Open 3(2), 1–14 (2017)
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Ohmoto, Y., Shimojo, S., Morita, J., Hayashi, Y. (2021). Investigating Clues for Estimating ICAP States Based on Learners’ Behavioural Data During Collaborative Learning. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_24
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DOI: https://doi.org/10.1007/978-3-030-80421-3_24
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