The effect of interaction granularity on learning with a data normalization tutor

Abstract

Intelligent Tutoring Systems (ITSs) have proven their effectiveness in many instructional domains, ranging in the complexity of domain theories and tasks students are to perform. The typical effect sizes achieved by ITSs are around 1SD, which are still low in comparison to the effectiveness of expert human tutors. Recently there have been several analyses done in order to identify the factors that contribute to success of human tutors, and to replicate it in ITSs. VanLehn [6] proposes that the crucial factor is the granularity of interaction: the lower the level of discussions between the (human or artificial) tutor and the student, the higher the effectiveness. We investigated the effect of interaction granularity in the context of NORMIT, a constraint-based tutor that teaches data normalization. Our study compared the standard version of NORMIT, which provided hints in response to errors, to a version which used adaptive tutorial dialogues instead. The results show that the interaction granularity hypothesis holds in our experimental situation, and that the effect size achieved is consistent with other reported studies of a similar nature.