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This paper describes an initial pilot study of Rimac, a natural-language tutoring system for physics. Rimac uses a student model to guide decisions about "what content to discuss next" during reflective dialogues that are initiated after students solve quantitative physics problems, and "how much support to provide" during these discussions, that is, domain contingent scaffolding and instructional contingent scaffolding, respectively. The pilot study compared an experimental and control version of Rimac. The experimental version uses students' responses to pretest items to initialize the student model and dynamically updates the model based on students' responses to tutor questions during reflective dialogues. It then decides what and how to discuss the next question based on the model predictions. The control version initializes its student model based on students' pretest performance but does not update the model further and assigns students to a fixed line of reasoning level based on the student model predictions. We hypothesized that students who used the experimental version of Rimac would achieve higher learning gains than students who used the control version. Although we did not find a significant difference in learning between conditions, the experimental group took significantly less time to complete the pilot study dialogues than did the control group. That is, the experimental condition led to more efficient learning, for both low and high prior knowledge level learners. We discuss this finding and describe future work to improve the tutor's potential to support student learning.

This paper discusses how a dialogue-based tutoring system makes decisions to proactively scaffold students during conceptual discussions about physics. The tutor uses a student model to predict the likelihood that the student will answer the next question in a dialogue script correctly. Based on these predictions, the tutor will, step by step, choose the granularity at which the next step in the dialogue is discussed. The tutor attempts to pursue the discussion at the highest possible level, with the goal of helping the student achieve mastery, but with the constraint that the questions it asks are within the student's ability to answer when appropriately supported; that is, the tutor aims to stay within its estimate of the student's zone of proximal development for the targeted concepts. The scaffolding provided by the tutor is further adapted by adjusting the way the questions are expressed.

In this paper, we propose a computational approach to model the Zone of Proximal Development (ZPD) using predicted probabilities of correctness and engaging students in reflective dialogue. To that end, we employ a predictive model that uses a linear function of a variety of parameters, including difficulty and student knowledge and we analyze the activity of students who use a natural language tutoring system that presents conceptual reflection questions after students solve high-school physics problems. In order to operationalize our approach, we introduce the concept of the "Grey Area", that is the area of uncertainty in which the student model cannot predict with acceptable accuracy whether a student is able to give a correct answer without support. We further discuss the impact of our approach on student modeling, the limitations of this work and we discuss future work in systematically and rigorously evaluating the approach.

In this paper, we propose a computational approach to modeling the Zone of Proximal Development of students who learn using a natural language tutoring system for physics. We employ a student model that predicts students' performance based on their prior knowledge and their activity when using a dialogue tutor to practice with conceptual, reflection questions about high-school physics. Furthermore, we introduce the concept of the "Grey Area", the area in which the student model cannot predict with acceptable accuracy whether a student has mastered the knowledge components or skills present in a particular step. We envision that our approach will contribute to the way we design learning content for ITSs and the way we author dialogues for natural-language tutoring systems. We further discuss the impact of our approach on student modeling and discuss future work in systematically and rigorously evaluating the approach.