Abstract
To improve the efficiency of teaching and learning, it is substantially important to know learners’ mental states during their learning processes. In this study, we tried to extract the relationships between the learner’s mental states and the learner’s physiological information complemented by the teacher’s speech acts using machine learning. The results of the system simulation showed that the system could estimate the learner’s mental states in high accuracy. Based on the construction of the system, we further discussed the concept of IMS and the necessary future work for IMS development.
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References
Takehana, K., Matsui, T.: Association rules on relationships between learner’s physiological information and mental states during learning process. In: Yamamoto, S. (ed.) HIMI 2016. LNCS, vol. 9735, pp. 209–219. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40397-7_21
Fujiyoshi, H., Yoshimura, K., Kunze, K., Kise, K.: Eibun mondai kaitoji no shiten joho o mochiita eigo noryoku suiteho [English ability estimation method using eye movement information during English question answering]. Tech. Rep. Inst. Electron. Inf. Commun. Eng. 115(25), 49–54 (2015)
Horiguchi, Y., Kojima, K., Matsui, T.: A method to estimate learners’ impasses based on features in low-level interactions by using MRA. In: Proceedings of the 58th SIG on Advanced Learning Science and Technology, pp. 1–6 (2010)
Kojima, K., Muramatsu, K., Matsui, T.: Experimental study on description of eye-movements among choices in answering to multiple-choice problems. Trans. Jpn. Soc. Inf. Syst. Educ. 31(2), 197–202 (2014)
D’Mello, S., Graesser, A., Picard, R.W.: Toward an affect-sensitive AutoTutor. IEEE Intell. Educ. Syst. 22(4), 53–61 (2007)
Matsui, T., Horiguchi, Y., Kojima, K., Akakura, T.: A study on exploration of relationships between behaviors and mental states of learners for value co-creative education and learning environment. In: Yamamoto, S. (ed.) HCI 2014. LNCS, vol. 8522, pp. 69–79. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07863-2_8
Ryu, H., Monk, A.: Analysing interaction problems with cyclic interaction theory: low-level interaction walkthrough. PsychNology J. 2(3), 304–330 (2004)
Fujie, Y.: Role of teacher’s repetition in classroom teaching. Jpn. J. Educ. Technol. 23(4), 201–212 (2000)
Kishi, T., Nojima, E.: A structural analysis of elementary school teachers’ and children’s utterances in Japanese classes. Jpn. J. Educ. Psychol. 54(3), 322–333 (2006)
Shimizu, Y., Uchida, N.: How do children adapt to classroom discourse? Quantitative and qualitative analyses of first grade homeroom activities. Jpn. J. Educ. Psychol. 49(3), 314–325 (2001)
Pekrun, R., Goetz, T., Frenzel, A.C., Barchfeld, P., Perry, R.P.: Measuring emotions in students’ learning and performance: the achievement emotions questionnaire (AEQ). Contemp. Educ. Psychol. 36(1), 36–48 (2011)
Tawatsuji, Y., Uno, T., Fang, S., Matsui, T.: Real-time estimation of learners’ mental states from learners’ physiological information using deep learning, In: Yang, J.C., et al. (eds.) Proceedings of the 26th International Conference on Computers in Education, pp. 107–109 (2018)
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Matsui, T., Tawatsuji, Y., Fang, S., Uno, T. (2019). Conceptualization of IMS that Estimates Learners’ Mental States from Learners’ Physiological Information Using Deep Neural Network Algorithm. In: Coy, A., Hayashi, Y., Chang, M. (eds) Intelligent Tutoring Systems. ITS 2019. Lecture Notes in Computer Science(), vol 11528. Springer, Cham. https://doi.org/10.1007/978-3-030-22244-4_9
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