Non-intrusive assessment of learners’ prior knowledge in dialogue-based intelligent tutoring systems


Goal and Scope: This article describes a study whose goal was to assess students’ prior knowledge level with respect to a target domain based solely on characteristics of the natural language interaction between students and conversational Intelligent Tutoring Systems (ITSs). We report results on data collected from two conversational ITSs: a micro-adaptive-only ITS and a fully-adaptive (micro- and macro-adaptive) ITS. These two ITSs are in fact different versions of the state-of-the-art conversational ITS DeepTutor (http://www.deeptutor.org). Approach and Results: Our models rely on both dialogue and session interaction features including time on task, student generated content features (e.g., vocabulary size or domain specific concept use), and pedagogy-related features (e.g., level of scaffolding measured as number of hints). Linear regression models were explored based on these features in order to predict students’ knowledge level, as measured with a multiple-choice pre-test, and yielded in the best cases an r=0.949 and adjusted r-square =0.833. We discuss implications of our findings for the development of future ITSs.

Publication Title

Smart Learning Environments