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.