Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation

• URL: http://arxiv.org/abs/2302.13496v1
• Date: Mon, 27 Feb 2023 03:43:25 GMT
• Title: Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation
• Authors: Lingzhi Wang, Mrinmaya Sachan, Xingshan Zeng, Kam-Fai Wong
• Abstract summary: We propose a unified framework that combines teaching response generation and pedagogical strategy prediction. Our experiments and analyses shed light on how teaching strategies affect dialog tutoring.
• Score: 35.11534904787774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog. CTSs have become a key pillar in educational data mining research. A key challenge in CTSs is to engage the student in the conversation while exposing them to a diverse set of teaching strategies, akin to a human teacher, thereby, helping them learn in the process. Different from previous work that generates responses given the strategies as input, we propose to jointly predict teaching strategies and generate tutor responses accordingly, which fits a more realistic application scenario. We benchmark several competitive models on three dialog tutoring datasets and propose a unified framework that combines teaching response generation and pedagogical strategy prediction, where a self-distillation mechanism is adopted to guide the teaching strategy learning and facilitate tutor response generation. Our experiments and analyses shed light on how teaching strategies affect dialog tutoring.

Related papers

• Exploring Knowledge Tracing in Tutor-Student Dialogues [53.52699766206808]
  We present a first attempt at performing knowledge tracing (KT) in tutor-student dialogues. We propose methods to identify the knowledge components/skills involved in each dialogue turn. We then apply a range of KT methods on the resulting labeled data to track student knowledge levels over an entire dialogue.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• SPL: A Socratic Playground for Learning Powered by Large Language Model [5.383689446227398]
  Socratic Playground for Learning (SPL) is a dialogue-based ITS powered by the GPT-4 model. SPL aims to enhance personalized and adaptive learning experiences tailored to individual needs.
  arXiv  Detail & Related papers  (2024-06-20T01:18:52Z)
• BIPED: Pedagogically Informed Tutoring System for ESL Education [11.209992106075788]
  Large Language Models (LLMs) have a great potential to serve as readily available and cost-efficient Conversational Intelligent Tutoring Systems (CITS) Existing CITS are designed to teach only simple concepts or lack the pedagogical depth necessary to address diverse learning strategies. We construct a BIlingual PEDagogically-informed Tutoring dataset of one-on-one, human-to-human English tutoring interactions.
  arXiv  Detail & Related papers  (2024-06-05T17:49:24Z)
• Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions [34.760230622675365]
  Intelligent tutoring systems (ITSs) imitate human tutors and aim to provide customized instructions or feedback to learners. With the emergence of generative artificial intelligence, large language models (LLMs) entitle the systems to complex and coherent conversational interactions. We investigate how pedagogical instructions facilitate the scaffolding in ITSs, by conducting a case study on guiding children to describe images for language learning.
  arXiv  Detail & Related papers  (2024-04-04T13:22:28Z)
• Opportunities and Challenges in Neural Dialog Tutoring [54.07241332881601]
  We rigorously analyze various generative language models on two dialog tutoring datasets for language learning. We find that although current approaches can model tutoring in constrained learning scenarios, they perform poorly in less constrained scenarios. Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring.
  arXiv  Detail & Related papers  (2023-01-24T11:00:17Z)
• Improving mathematical questioning in teacher training [1.794107419334178]
  High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. This paper builds a text-based interactive conversational agent to help teachers practice mathematical questioning skills.
  arXiv  Detail & Related papers  (2021-12-02T05:33:03Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. This paper reviews the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task. In addition, we categorize dialogue-related pre-training techniques which are employed to enhance PrLMs in dialogue scenarios.
  arXiv  Detail & Related papers  (2021-10-11T03:52:37Z)
• Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems [58.724629408229205]
  We demonstrate how traditional supervised learning and a simulator-free adversarial learning method can be used to achieve performance comparable to state-of-the-art RL-based methods. Our main goal is not to beat reinforcement learning with supervised learning, but to demonstrate the value of rethinking the role of reinforcement learning and supervised learning in optimizing task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2020-09-21T12:04:18Z)
• Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms [50.19997675066203]
  We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
  arXiv  Detail & Related papers  (2020-05-16T02:17:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.