Title

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)

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