Abstract
We present in this paper the findings of a study on the role of macro-adaptation in conversational intelligent tutoring. Macro-adaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Micro-adaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. We compared an intelligent tutoring system that offers both macro- and micro-adaptivity (fully-adaptive) with an intelligent tutoring system that offers only micro-adaptivity. Experimental data analysis revealed that learning gains were significantly higher for students randomly assigned to the fully-adaptive intelligent tutor condition compared to the micro-adaptive-only condition.
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Rus, V., Stefanescu, D., Baggett, W., Niraula, N., Franceschetti, D., Graesser, A.C. (2014). Macro-adaptation in Conversational Intelligent Tutoring Matters. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_29
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DOI: https://doi.org/10.1007/978-3-319-07221-0_29
Publisher Name: Springer, Cham
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