Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding

• URL: http://arxiv.org/abs/2603.24535v1
• Date: Wed, 25 Mar 2026 17:11:56 GMT
• Title: Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding
• Abstract summary: Adaptive scaffolding enhances learning, yet the field lacks robust methods for measuring it within authentic tutoring dialogue.<n>We introduce an embedding-based approach that analyzes scaffolding dynamics by aligning the semantics of dialogue turns, problem statements, and correct solutions.<n>We apply this framework to 1,576 real-world mathematics tutoring dialogues from the Eedi Question Anchored Tutoring Dialogues dataset.
• Score: 9.91280795515591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive scaffolding enhances learning, yet the field lacks robust methods for measuring it within authentic tutoring dialogue. This gap has become more pressing with the rise of remote human tutoring and large language model-based systems. We introduce an embedding-based approach that analyzes scaffolding dynamics by aligning the semantics of dialogue turns, problem statements, and correct solutions. Specifically, we operationalize alignment by computing cosine similarity between tutor and student contributions and task-relevant content. We apply this framework to 1,576 real-world mathematics tutoring dialogues from the Eedi Question Anchored Tutoring Dialogues dataset. The analysis reveals systematic differences in task alignment and distinct temporal patterns in how participants ground their contributions in problem and solution content. Further, mixed-effects models show that role-specific semantic alignment predicts tutorial progression beyond baseline features such as message order and length. Tutor contributions exhibited stronger grounding in problem content early in interactions. In contrast, student solution alignment was modestly positively associated with progression. These findings support scaffolding as a continuous, role-sensitive process grounded in task semantics. By capturing role-specific alignment over time, this approach provides a principled method for analyzing instructional dialogue and evaluating conversational tutoring systems.

Related papers

• Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning [57.22705949022221]
  RoleJudge is an evaluation framework that leverages audio large language models to assess the alignment between speech and character.<n>We introduce RoleChat, the first voice role-playing evaluation dataset enriched with chain-of-thought reasoning annotations.
  arXiv  Detail & Related papers  (2026-04-15T12:39:03Z)
• Towards Actionable Pedagogical Feedback: A Multi-Perspective Analysis of Mathematics Teaching and Tutoring Dialogue [6.13173513227026]
  We propose a multi-perspective discourse analysis that integrates domain-specific talk moves with dialogue act.<n>Our framework may prove helpful for providing human educator feedback, but also aiding in the development of AI agents.
  arXiv  Detail & Related papers  (2025-05-12T00:48:17Z)
• Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse [6.1701318546149]
  This paper focuses on analyzing mathematics tutoring discourse using talk moves.<n> scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task.
  arXiv  Detail & Related papers  (2024-12-18T00:13:04Z)
• Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue [37.618612723025784]
  In dialogue systems, discourse plays a crucial role in managing conversational focus and coordinating interactions.<n>It consists of two key structures: rhetorical structure and topic structure.<n>We introduce a unified representation that integrates rhetorical and topic structures, ensuring semantic consistency between them.<n>We propose an unsupervised mutual learning framework (UMLF) that jointly models rhetorical and topic structures, allowing them to mutually reinforce each other without requiring additional annotations.
  arXiv  Detail & Related papers  (2024-05-30T08:10:50Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues [8.652711997920463]
  Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. This study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks.
  arXiv  Detail & Related papers  (2023-01-12T08:37:00Z)
• Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue [22.103162555263143]
  We introduce contrastive learning and multi-task learning to jointly model the problem. Our proposed model achieves state-of-the-art performance on several public datasets.
  arXiv  Detail & Related papers  (2022-03-22T10:13:27Z)
• Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding [53.377028000325424]
  We propose an Iterative Alignment Network (IA-Net) for temporal sentence grounding task. We pad multi-modal features with learnable parameters to alleviate the nowhere-to-attend problem of non-matched frame-word pairs. We also devise a calibration module following each attention module to refine the alignment knowledge.
  arXiv  Detail & Related papers  (2021-09-14T02:08:23Z)
• Structured Attention for Unsupervised Dialogue Structure Induction [110.12561786644122]
  We propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias.
  arXiv  Detail & Related papers  (2020-09-17T23:07:03Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)
• Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning [50.5572111079898]
  Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks.
  arXiv  Detail & Related papers  (2020-02-27T04:36:52Z)

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.