Abstract
Collaborative game-based learning environments offer significant promise for creating effective and engaging group learning experiences. These environments enable small groups of students to work together toward a common goal by sharing information, asking questions, and constructing explanations. However, students periodically disengage from the learning process, which negatively affects their learning, and the impacts are more severe in collaborative learning environments as disengagement can propagate, affecting participation across the group. Here, we introduce a multimodal behavioral disengagement detection framework that uses facial expression analysis in conjunction with natural language analyses of group chat. We evaluate the framework with students interacting with a collaborative game-based learning environment for middle school science education. The multimodal behavioral disengagement detection framework integrating both facial expression and group chat modalities achieves higher levels of predictive accuracy than those of baseline unimodal models.
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Aslan, S., et al.: Human expert labeling process (HELP): towards a reliable higher-order user state labeling process and tool to assess student engagement. Educ. Technol. 57, 53–59 (2017)
Giannakos, M.N., Sharma, K., Pappas, I.O., Kostakos, V., Velloso, E.: Multimodal data as a means to understand the learning experience. Int. J. Inf. Manag. 48, 108–119 (2019)
Jeong, H., Hmelo-Silver, C.E., Jo, K.: Ten years of computer-supported collaborative learning: a meta-analysis of CSCL in STEM education during 2005–2014. Educ. Res. Rev. 28, 100284 (2019)
de Jesus, Â.M., Silveira, I.F.: A collaborative game-based learning framework to improve computational thinking skills. In: 2019 International Conference on Virtual Reality and Visualization (ICVRV), pp. 161–166. IEEE (2019)
Langer-Osuna, J.M.: Productive disruptions: rethinking the role of off-task interactions in collaborative mathematics learning. Educ. Sci. 8(2), 87 (2018)
Park, K., et al.: Detecting disruptive talk in student chat-based discussion within collaborative game-based learning environments. In: Proceedings of the 11th International Learning Analytics and Knowledge Conference, pp. 405–415 (2021)
Thomas, C., Jayagopi, D.B.: Predicting student engagement in classrooms using facial behavioral cues. In: Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, pp. 33–40 (2017)
Acknowledgements
This work is supported by the National Science Foundation through grants IIS-1839966, and SES-1840120. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Fahid, F.M. et al. (2022). Multimodal Behavioral Disengagement Detection for Collaborative Game-Based Learning. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_38
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DOI: https://doi.org/10.1007/978-3-031-11647-6_38
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