Dan Stefanescu

NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity

We present in this paper our system developed for SemEval 2015 Shared Task 2 (2a - English Semantic Textual Similarity, STS, and 2c - Interpretable Similarity) and the results of the submitted runs. For the English STS subtask, we used regression models combining a wide array of features including semantic similarity scores obtained from various methods. One of our runs achieved weighted mean correlation score of 0.784 for sentence similarity subtask (i.e., English STS) and was ranked tenth among 74 runs submitted by 29 teams. For the interpretable similarity pilot task, we employed a rule-based approach blended with chunk alignment labeling and scoring based on semantic similarity features. Our system for interpretable text similarity was among the top three best performing systems.

Macro-adaptation in Conversational Intelligent Tutoring Matters

We present in this paper the findings of a study on the role of macro-adaptation in conversational intelligent tutoring. Macro-adaptivity refers to a systems capability to select appropriate instructional tasks for the learner to work on. Micro-adaptivity refers to a systems capability to adapt its scaffolding while the learner is working on a particular task. We compared an intelligent tutoring system that offers both macro- and micro-adaptivity fully-adaptive with an intelligent tutoring system that offers only micro-adaptivity. Experimental data analysis revealed that learning gains were significantly higher for students randomly assigned to the fully-adaptive intelligent tutor condition compared to the micro-adaptive-only condition.

DeepTutor: towards macro- and micro-adaptive conversational intelligent tutoring at scale

We present an overview of the design of a conversational intelligent tutoring system, called DeepTutor, based on the framework of Learning Progressions. Learning Progressions capture students successful paths towards mastery. The assumption of the proposed tutor is that by guiding instruction based on Learning Progressions, the system will be more effective (and efficient for that matter).

Toward Non-intrusive Assessment in Dialogue-Based Intelligent Tutoring Systems

This paper 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 a state-of-the-art conversational ITS. We report results on data collected from two conversational ITSs: a micro-adaptive-only ITS and a fully adaptive (micro- and macro-adaptive) ITS. 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 = 0.878.