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Exploring real-time student models based on natural-language tutoring sessions

Natural language tutoring systems generate significant data during their tutoring sessions, which is often not used to inform real-time, persistent student models. The current research explores the feasibility of mapping concept-focused tutoring sessions to knowledge components, by breaking sessions down into features that are integrated into a session score. Three classes of tutoring conversation features were studied: semantic match of student contributions to domain content, tutor support (e.g., hints and prompts), and student verbosity (i.e., word counts). Analysis of the relative importance of these features and the ability of these features to predict later task performance on similar topics was conducted. Reinforcing prior work, semantic match scores were a key predictor for later test performance. Tutor help features (hints, prompts) were also useful secondary predictors. Unlike some related work, verbosity was a key predictor even after accounting for the semantic match.