Abstract
Goal setting and planning are integral components of self-regulated learning. Many students struggle to set meaningful goals and build relevant plans. Adaptive learning environments show significant potential for scaffolding students’ goal setting and planning processes. An important requirement for such scaffolding is the ability to perform student plan recognition, which involves recognizing students’ goals and plans based upon the observations of their problem-solving actions. We introduce a novel plan recognition framework that leverages trace log data from student interactions within a game-based learning environment called CRYSTAL ISLAND, in which students use a drag-and-drop planning support tool that enables them to externalize their science problem-solving goals and plans prior to enacting them in the learning environment. We formalize student plan recognition in terms of two complementary tasks: (1) classifying students’ selected problem-solving goals, and (2) classifying the sequences of actions that students indicate will achieve their goals. Utilizing trace log data from 144 middle school students’ interactions with CRYSTAL ISLAND, we evaluate a range of machine learning models for student goal and plan recognition. All machine learning-based techniques outperform the majority baseline, with LSTMs outperforming other models for goal recognition and naive Bayes performing best for plan recognition. Results show the potential for automatically recognizing students’ problem-solving goals and plans in game-based learning environments, which has implications for providing adaptive support for student self-regulated learning.
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References
Azevedo, R., Wiedbusch, M.: Theories of metacognition and pedagogy applied in AIED systems. In: du Boulay (ed.) Handbook of Artificial Intelligence in Education. Springer, The Netherlands (in Press)
Baikadi, A., et al.: Towards a computational model of narrative visualization. In: Workshops at the Seventh Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 2–9, October 2011
Blaylock, N., Allen, J.: Corpus-based, statistical goal recognition. In: International Joint Conference on Artificial Intelligence, vol. 3, pp. 1303–1308, August 2003
Boekaerts, M., Pekrun, R.: Emotions and emotion regulation in academic settings. Handb. Educ. Psychol. 3, 76–90 (2015)
Bholowalia, P., Kumar, A.: EBK-means: a clustering technique based on elbow method and k-means in WSN. Int. J. Comput. Appl. 105(9) (2014)
Conati, C., Gertner, A., Vanlehn, K.: Using Bayesian networks to manage uncertainty in student modeling. User Model. User-Adap. Inter. 12(4), 371–417 (2002)
Geib, C., Steedman, M.: On natural language processing and plan recognition. In: International Joint Conference on Artificial Intelligence, vol. 2007, pp. 1612–1617, January 2007
Ghallab, M., Nau, D., Traverso, P.: Automated Planning and Acting. Cambridge University Press (2016)
Graesser, A., McNamara, D.: Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educ. Psychol. 45(4), 234–244 (2010)
Ha, E.Y., Rowe, J.P., Mott, B.W., Lester, J.C.: Goal recognition with Markov logic networks for player-adaptive games. In: Seventh Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 32–39, October 2011
Liu, S.M., Chen, J.H.: A multi-label classification based approach for sentiment classification. Expert Syst. Appl. 42(3), 1083–1093 (2015)
Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084–3104 (2012)
Min, W., Ha, E.Y., Rowe, J., Mott, B., Lester, J.: Deep learning-based goal recognition in open-ended digital games. In: Tenth Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 37–43, September 2014
Min, W., et al. Multimodal goal recognition in open-world digital games. In: Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference, pp. 80–86, September 2017
Min, W., Mott, B.W., Rowe, J.P., Liu, B., Lester, J.C.: Player goal recognition in open-world digital games with long short-term memory networks. In: Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 2590–2596, July 2016
Pereira, R.B., Plastino, A., Zadrozny, B., Merschmann, L.H.: Correlation analysis of performance measures for multi-label classification. Inf. Process. Manage. 54(3), 359–369 (2018)
Schunk, D.H., Greene, J.A.: Historical, Contemporary, and Future Perspectives on Self-Regulated Learning and Performance, pp. 1–15. Routledge, New York (2017)
Srinivasa-Desikan, B.: Natural Language Processing and Computational Linguistics: A Practical Guide to Text Analysis with Python, Gensim, spaCy, and Keras. Packt Publishing Ltd.. (2018)
Weintrop, D., Wilensky, U.: Transitioning from introductory block-based and text-based environments to professional programming languages in high school computer science classrooms. Comput. Educ. 142, 103646 (2019)
McComas, W.F.: Metacognition. In: McComas, W.F. (ed.) The language of science education, pp. 63–63. SensePublishers, Rotterdam (2014). https://doi.org/10.1007/978-94-6209-497-0_55
Winne, P.H., Hadwin, A.E.: Studying as Self-Regulated Learning, pp. 291–318. Routledge, New York (2018)
Winne, P.H., Hadwin, A.F.: The weave of motivation and self-regulated learning. In: Schunk, D.H., Zimmerman, E.J. (eds.) Motivation and Self-Regulated Learning: Theory, Research, and Applications, pp. 297–314. Lawrence Erlbaum Associates Publishers (2008)
Winne, P.H.: Theorizing and researching levels of processing in self-regulated learning. Br. J. Educ. Psychol. 88(1), 9–20 (2018)
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This research was supported by funding from the National Science Foundation under grant DUE-1761178. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
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Goslen, A., Carpenter, D., Rowe, J.P., Henderson, N., Azevedo, R., Lester, J. (2022). Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_7
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