Macro-adaptation in conversational intelligent tutoring matters
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. © 2014 Springer International Publishing Switzerland.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Rus, V., Stefanescu, D., Baggett, W., Niraula, N., Franceschetti, D., & Graesser, A. (2014). Macro-adaptation in conversational intelligent tutoring matters. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8474 LNCS, 242-247. https://doi.org/10.1007/978-3-319-07221-0_29
