Adapting Step Granularity In Tutorial Dialogue Based On Pretest Scores

We explore the effectiveness of adaptively deciding whether to further decompose a step in a line of reasoning during tutorial dialogue based on students' pretest scores. We compare two versions of a tutorial dialogue system in high school classrooms: one that always decomposes a step to its simplest substeps and one that adaptively decides to decompose a step based on a student's performance on pretest items that target the knowledge required to correctly answer that step. We hypothesize that students using the two versions of the tutoring system will learn similarly but that students who use the version that adaptively decomposes a step will learn more efficiently. Our results from classroom studies suggest support for our hypothesis. While students learned similarly and with similar efficiency across conditions, high prior knowledge students in the adaptive condition learned significantly more efficiently than high prior knowledge students in the control condition and learned similar amounts.