Detecting Collaborative Learning Through Emotions: An Investigation Using Facial Expression Recognition
Providing adaptive feedback to learners engaging in collaborative learning activities is one research topic in the development of intelligent tutoring systems. However, there is a need to investigate how to systematically evaluate a learner’s activities and provide feedback on them. The present study investigates how emotional states, detected through facial recognition, can be utilized to capture the learning process in a simple jigsaw-type collaborative task. It was predicted that when learners argue with each other and reason deeply, they may experience several emotional states such as positive and negative states. The results show that when learners work harder on developing a mutual understanding through conflictive interaction, negative emotions can be used to predict this process. This study contributes to the knowledge of how emotional states detected by facial recognition technology can be applied to predict learning process in conflictive tasks. Moreover, these empirical results will impact the development of adaptive feedback mechanisms for intelligent tutoring systems for collaborative learning.
KeywordsCollaborative learning Pedagogical conversational agents Emotion Learning assessment
This work was supported by Grant-in-Aid for Scientific Research (C), Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 16K00219.
- 1.Aronson, E., Patnoe, S.: The Jigsaw Classroom: Building Cooperation in the Classroom, 2nd edn. Addison Wesley Longman, New York (1997)Google Scholar
- 4.Chi, M., Leeuw, N., Chiu, M., Lavancher, C.: Eliciting self-explanations improves understanding. Cogn. Sci. 18(3), 439–477 (1994)Google Scholar
- 12.Hayashi, Y.: Coordinating knowledge integration with pedagogical agents: effects of agent gaze gestures and dyad synchronization. In: Proceeding of the 13th International Conference on Intelligent Tutoring Systems (ITS 2016), pp. 254–259 (2016)Google Scholar
- 15.Hesse, F., Care, E., Buder, J., Sassenberg, K., Griffin, P.: A framework for teachable collaborative problem solving skills. In: Griffin, P., Care, E. (eds.) Assessment and Teaching of 21st Century Skills. EAIA, pp. 37–56. Springer, Dordrecht (2015). https://doi.org/10.1007/978-94-017-9395-7_2CrossRefGoogle Scholar
- 16.Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. Int. J. Artif. Intell. Educ. (IJAIED) 8, 30–43 (1997)Google Scholar
- 17.Kuilenburg, v.H., Wiering, M., Uyl, d.M.: A model based method for automatic facial expression recognition. In: Proceedings of the European Conference on Machine Learning (ECML2005), pp. 194–205 (2005)Google Scholar
- 20.Leelawong, K., Biswas, G.: Designing learning by teaching agents: the Betty’s brain system. Int. J. Artif. Intell. Educ. 18(3), 181–208 (2008)Google Scholar
- 22.McNamara, D., O’Reilly, T., Rowe, M.: iSTART: A Web-Based Tutor that Teaches Self-explanation and Metacognitive Reading Strategies. Lawrence Erlbaum Associates, Mahwah (2007)Google Scholar
- 26.OECD: PISA 2015 Results (Volume V): Collaborative Problem Solving. OECD Publishing, Paris (2017). https://doi.org/10.1787/9789264285521-en
- 29.Vygotsky, L.S.: The Development of Higher Psychological Processes. Harvard University Press, Cambridge (1980)Google Scholar