Estimation of ICAP States Based on Interaction Data During Collaborative Learning
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Published
Dec 6, 2024
Yoshimasa Ohmoto
Shigen Shimojo
Junya Morita
Yugo Hayashi
https://orcid.org/0000-0002-9451-8358
Abstract
The primary goal of this study is to investigate a method for estimating the state of learners in the near future using nonverbal information used in multimodal interaction as cues to provide adaptive support in collaborative learning. We used interactive-constructive-active-passive (ICAP) theory to classify learners' states in collaborative learning. We attempted to determine whether a learner’s ICAP state was passive based on multimodal data obtained during a collaborative concept-map task. We conducted an experiment on collaborative learning among learners and acquired data on conversational type, the results of learning performance (pre- and post-tests), utterances, facial expressions, gaze, and voice during the experiment. We conducted two analyses. One was sequential pattern mining, to obtain clues for predicting the participants' state after 5 seconds. The other was a support vector machine to try to classify the participants' state based on the obtained clues. We found several candidates that could be used for learner-state estimation in the near future. The learner-state estimation using multimodal information yielded higher than 70% accuracy. In contrast, there were differences in the ease of estimating each pair's learning state. It appears that capturing the characteristics of interactions in collaborative learning for each pair is necessary for a more accurate estimation of the learners' state.
How to Cite
Ohmoto, Y., Shimojo, S., Morita, J., & Hayashi, Y. (2024). Estimation of ICAP States Based on Interaction Data During Collaborative Learning. Journal of Educational Data Mining, 16(2), 149–176. https://doi.org/10.5281/zenodo.14283893
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Keywords
multimodal information, learner state estimation, human behavior analysis, collaborative learning, concept map, ICAP theory, Computer-Supported Collaborative Learning (CSCL)
References
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Blikstein, P. and Worsley, M. 2016. Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics 3, 2, 220–238.
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Boersma, P. 2001. Praat, a system for doing phonetics by computer. Glot International 5, 9, 341–345.
Cai, Y., Shimojo, S., and Hayashi, Y. 2020. Observing facial muscles to estimate the learning state during collaborative learning: A focus on the ICAP framework. In Proceedings of the 28th International Conference on Computers in Education (ICCE2020). 119–126.
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Bodemer, D. 2011. Tacit guidance for collaborative multimedia learning. Computers in Human Behavior 27, 3, 1079–1086.
Boersma, P. 2001. Praat, a system for doing phonetics by computer. Glot International 5, 9, 341–345.
Cai, Y., Shimojo, S., and Hayashi, Y. 2020. Observing facial muscles to estimate the learning state during collaborative learning: A focus on the ICAP framework. In Proceedings of the 28th International Conference on Computers in Education (ICCE2020). 119–126.
Carbonell, J. R. 1970. Ai in cai: An artificial-intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems 11, 4, 190–202.
Chi, M. T. 2009. Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science 1, 1, 73–105.
Chi, M. T., Adams, J., Bogusch, E. B., Bruchok, C., Kang, S., Lancaster, M., Levy, R., Li, N., McEldoon, K. L., Stump, G. S., et al. 2018. Translating the ICAP theory of cognitive engagement into practice. Cognitive Science 42, 6, 1777–1832.
Chi, M. T., De Leeuw, N., Chiu, M.-H., and LaVancher, C. 1994. Eliciting self-explanations improves understanding. Cognitive Science 18, 3, 439–477.
Chi, M. T. and Wylie, R. 2014. The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist 49, 4, 219–243.
Cho, J. and Kato, S. 2011. Detecting emotion from voice using selective bayesian pairwise classifiers. In 2011 IEEE Symposium on Computers & Informatics. IEEE, 90–95.
Dillenbourg, P., Baker, M., Blaye, A., and O’malley, C. 1996. The evolution of research on collaborative learning. In Learning in Humans and Machines: Towards an Interdisciplinary Learning Science, H. Spada and P. Reimann, Eds. Elsevier, 189–211.
Dillenbourg, P. and Fischer, F. 2007. Computer-supported collaborative learning: The basics. Zeitschrift für Berufs-und Wirtschaftspädagogik 21, 111–130.
D’Mello, S. K., Craig, S. D., and Graesser, A. C. 2009. Multimethod assessment of affective experience and expression during deep learning. International Journal of Learning Technology 4, 3-4, 165–187.
Ekman, P., Freisen, W. V., and Ancoli, S. 1980. Facial signs of emotional experience. Journal of Personality and Social Psychology 39, 6, 1125.
Ekman, P. and Friesen, W. V. 1976. Measuring facial movement. Environmental Psychology and Nonverbal Behavior 1, 1, 56–75.
Engelmann, T. and Hesse, F. W. 2010. How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving. International Journal of Computer-Supported Collaborative Learning 5, 3, 299–319.
Fournier-Viger, P., Gueniche, T., and Tseng, V. S. 2012. Using partially-ordered sequential rules to generate more accurate sequence prediction. In Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings 8. Springer, 431–442.
Fournier-Viger, P., Nkambou, R., and Nguifo, E. M. 2008. A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems. In MICAI 2008: Advances in Artificial Intelligence, A. Gelbukh and E. F. Morales, Eds. Springer Berlin Heidelberg, Berlin, Heidelberg, 765–778.
Goldberg, P., Sümer, Ö., Stürmer, K., Wagner, W., Göllner, R., Gerjets, P., Kasneci, E., and Trautwein, U. 2021. Attentive or not? toward a machine learning approach to assessing students’ visible engagement in classroom instruction. Educational Psychology Review 33, 27–49.
Graesser, A. and McNamara, D. 2010. Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist 45, 4, 234–244.
Gweon, G., Rose, C., Carey, R., and Zaiss, Z. 2006. Providing support for adaptive scripting in an on-line collaborative learning environment. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, and G. Olson, Eds. CHI ’06. Association for Computing Machinery, New York, NY, USA, 251–260.
Hayashi, Y. 2018. Gaze feedback and pedagogical suggestions in collaborative learning. In Intelligent Tutoring Systems, R. Nkambou, R. Azevedo, and J. Vassileva, Eds. Springer International Publishing, Cham, 78–87.
Hayashi, Y. 2019a. Multiple pedagogical conversational agents to support learner-learner collaborative learning: Effects of splitting suggestion types. Cognitive Systems Research 54, 246–257.
Hayashi, Y. 2019b. Towards supporting collaborative learning with an intelligent tutoring system: Predicting learning process by using gaze and verbal information. Bulletin of the Japanese Cognitive Science Society 26, 3, 343–356.
Hayashi, Y. 2020. Gaze awareness and metacognitive suggestions by a pedagogical conversational agent: an experimental investigation on interventions to support collaborative learning process and performance. International Journal of Computer-Supported Collaborative Learning 15, 4, 469–498.
Hayashi, Y. and Miwa, K. 2011. Understanding other’s perspectives in conflict communication. Cognitive Studies: Bulletin of the Japanese Cognitive Science Society 18, 4, 569–584.
Hayashi, Y., Miwa, K., and Morita, J. 2007. A laboratory study on collaborative problem solving by taking different perspectives. Cognitive Studies 14, 4, 604–619.
Hsiao, J.-C., Chen, S.-K., Chen, W., and Lin, S. S. 2022. Developing a plugged-in class observation protocol in high-school blended stem classes: Student engagement, teacher behaviors and student-teacher interaction patterns. Computers & Education 178, 104403.
Iliou, T. and Anagnostopoulos, C.-N. 2010. Classification on speech emotion recognition-a comparative study. Animation 4, 5.
Jordan, P., Ringenberg, M., and Hall, B. 2006. Rapidly developing dialogue systems that support learning studies. In Proceedings of ITS06 Workshop on Teaching with Robots, Agents, and NLP. Springer, 1–8.
Järvelä, S., Dindar, M., Sobocinski, M., and Nguyen, A. 2022. Chapter 16: Multimodal research for studying collaborative learning in higher education. In Handbook of Digital Higher Education, R. Sharpe, S. Bennett, and T. Varga-Atkins, Eds. Edward Elgar Publishing, Cheltenham, UK, 199 – 210.
Kasparova, A., Celiktutan, O., and Cukurova, M. 2021. Inferring student engagement in collaborative problem solving from visual cues. In Companion Publication of the 2020 International Conference on Multimodal Interaction. ICMI ’20 Companion. Association for Computing Machinery, New York, NY, USA, 177–181.
Koedinger, K. R., Anderson, J. R., Hadley, W. H., Mark, M. A., et al. 1997. Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8, 1, 30–43.
Lin, J., Keogh, E., Wei, L., and Lonardi, S. 2007. Experiencing sax: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15, 107–144.
Ma, Y., Celepkolu, M., and Boyer, K. E. 2022. Detecting impasse during collaborative problem solving with multimodal learning analytics. In LAK22: 12th International Learning Analytics and Knowledge Conference. LAK22. Association for Computing Machinery, New York, NY, USA, 45–55.
McDaniel, M. A., Anderson, J. L., Derbish, M. H., and Morrisette, N. 2007. Testing the testing effect in the classroom. European Journal of Cognitive Psychology 19, 4-5, 494–513.
Misu, T., Sugiura, K., Kawahara, T., Ohtake, K., Hori, C., Kashioka, H., Kawai, H., and Nakamura, S. 2011. Modeling spoken decision support dialogue and optimization of its dialogue strategy. ACM Transactions on Speech and Language Processing (TSLP) 7, 3, 1–18.
Miyake, N. 1986. Constructive interaction and the iterative process of understanding. Cognitive Science 10, 2, 151–177.
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., and Ghazi Saeedi, M. 2021. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments 29, 1, 142–163.
Nasu, M. 1988. A study of weiner’s attribution theory of achievement motivation. Japanese Journal of Educational Psychology 37, 1, 84–95.
Ohmoto, Y., Shimojo, S., Morita, J., and Hayashi, Y. 2021. Investigating clues for estimating ICAP states based on learners’ behavioural data during collaborative learning. In Intelligent Tutoring Systems, A. I. Cristea and C. Troussas, Eds. Springer International Publishing, Cham, 224–231.
Okada, T. and Simon, H. A. 1997. Collaborative discovery in a scientific domain. Cognitive Science 21, 2, 109–146.
O’Neill, I. M., Hanna, P., Liu, X., and McTear, M. F. 2003. The queen’s communicator: an object-oriented dialogue manager. In 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA, 593–596.
Raux, A., Langner, B., Bohus, D., Black, A., and Eskenazi, M. 2005. Let’s go public! taking a spoken dialog system to the real world. In Proceedings of Interspeech 2005. ISCA, 885–888.
Rummel, N. and Spada, H. 2005. Learning to collaborate: An instructional approach to promoting collaborative problem solving in computer-mediated settings. The journal of the Learning Sciences 14, 2, 201–241.
Rummel, N., Spada, H., and Hauser, S. 2009. Learning to collaborate while being scripted or by observing a model. International Journal of Computer-Supported Collaborative Learning 4, 69–92.
Rummel, N., Weinberger, A., Wecker, C., Fischer, F., Meier, A., Voyiatzaki, E., Kahrimanis, G., Spada, H., Avouris, N., Walker, E., Koedinger, K. R., Rosé, C. P., Kumar, R., Gweon, G., Wang, Y.-C., and Joshi, M. 2008. New challenges in cscl: towards adaptive script support. In Proceedings of the 8th International Conference on International Conference for the Learning Sciences - Volume 3. ICLS’08. International Society of the Learning Sciences, 338–345.
Sangin, M., Molinari, G., Nüssli, M.-A., and Dillenbourg, P. 2011. Facilitating peer knowledge modeling: Effects of a knowledge awareness tool on collaborative learning outcomes and processes. Computers in Human Behavior 27, 3, 1059–1067.
Scherer, K. R., London, H., and Wolf, J. J. 1973. The voice of confidence: Paralinguistic cues and audience evaluation. Journal of Research in Personality 7, 1, 31–44.
Schneider, B. and Blikstein, P. 2014. Unraveling students’ interaction around a tangible interface using gesture recognition. In 7th International Conference on Educational Data Mining, J. Stamper, Z. Pardos, M. Mavrikis, and B. M. McLaren, Eds. Educational Data Mining Society, 320–323.
Schneider, B. and Pea, R. 2013. Real-time mutual gaze perception enhances collaborative learning and collaboration quality. International Journal of Computer-Supported Collaborative Learning 8, 4, 375–397.
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