Abstract
Accurate classification of learner responses is a critical component of dialog based tutoring systems (DBT). Errors in identifying the intent and context of responses can have cascading effects on the ongoing interaction thereby affecting the learning experience and outcome. In this paper we attempt to quantify the impact of Tutor misclassifications on student behavior by analyzing differences across our hypothesized conditions namely, no-misclassification vs. misclassification using various dialog metrics. We find that not only are there significant changes in behavior across the two groups but that Tutor errors related to misunderstanding of Intent - although fewer in occurrence, appear to have a higher impact than a misclassification of a valid student answer. We also see some evidence of the effectiveness of scaffolds like FITBs in sustaining dialog thereby mitigating the effects of a Tutor error.
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References
Baker, R.S.: Modeling and understanding students off-task behavior in intelligent tutoring systems. In: Proceedings of SIGCHI Conference Human Factors, Computing Systems, pp. 1059–1068 (2007)
Beck, J.: Engagement tracing: using response times to model student disengagement. In: Proceedings of 12th International Conference Artificial Intelligence in Education (AIED 2005), pp. 88–95 (2005)
Cetintas, S., Si, L., Xin, Y.P., Hord, C.: Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques. IEEE Trans. Learn. Technol. 3(3), 228–236 (2010)
Dzikovska, M.O., et al.: SemEval-2013 task 7: the joint student response analysis and 8th recognizing textual entailment challenge. In: The First Joint Conference on Lexical and Computational Semantics, SEM 2013, Atlanta, Georgia, USA, 13–14 June. Association for Computational Linguistics (2013)
Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H.H., Ventura, M., Olney, A., Louwerse, M.M.: AutoTutor: a tutor with dialogue in natural language. Behav. Res. Methods Instrum. Comput. 36(2), 180–192 (2004)
Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106(4), 901–918 (2014)
VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46, 197–221 (2011)
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Afzal, S., Shashidhar, V., Sindhgatta, R., Sengupta, B. (2018). Impact of Tutor Errors on Student Engagement in a Dialog Based Intelligent Tutoring System. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_26
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DOI: https://doi.org/10.1007/978-3-319-91464-0_26
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