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

摘要

Abstract 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.