A Unified Response Framework for Questions and Tutoring Dialogues
In this work, we propose a unified framework that models both student responses to questions and tutor-posed subtasks during dialogues on a shared item response theory (IRT) scale.
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Key points
- Existing student models in educational applications largely focus on student responses to questions.
- We first identify mathematical subtasks in tutoring dialogues, rewrite them into self-contained questions, and identify the correctness of student responses to these subtasks.
- We then develop an IRT framework that estimates question/subtask difficulty from text, tracks students' evolving abilities across question-answering and dialogue interactions, and a novel method to leverages tutor moves between consecutive subtasks to regularize their difficulty values.
- We evaluate our framework on two real-world math education datasets containing both human- and AI-tutored dialogues.
Sources (1)
- [1]A Unified Response Framework for Questions and Tutoring DialoguesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:47 AM
In this work, we propose a unified framework that models both student responses to questions and tutor-posed subtasks during dialogues on a shared item response theory (IRT) scale.
Existing student models in educational applications largely focus on student responses to questions.
Extractive summary: sentences quoted from the sources.