Date
Publisher
arXiv
Effective feedback is essential for refining instructional practices in mathematics education, and researchers often turn to advanced natural language processing (NLP) models to analyze classroom dialogues from multiple perspectives. However, utterance-level discourse analysis encounters two primary challenges: (1) multifunctionality, where a single utterance may serve multiple purposes that a single tag cannot capture, and (2) the exclusion of many utterances from domain-specific discourse move classifications, leading to their omission in feedback. To address these challenges, we proposed a multi-perspective discourse analysis that integrates domain-specific talk moves with dialogue act (using the flattened multi-functional SWBD-MASL schema with 43 tags) and discourse relation (applying Segmented Discourse Representation Theory with 16 relations). Our top-down analysis framework enables a comprehensive understanding of utterances that contain talk moves, as well as utterances that do not contain talk moves. This is applied to two mathematics education datasets: TalkMoves (teaching) and SAGA22 (tutoring). Through distributional unigram analysis, sequential talk move analysis, and multi-view deep dive, we discovered meaningful discourse patterns, and revealed the vital role of utterances without talk moves, demonstrating that these utterances, far from being mere fillers, serve crucial functions in guiding, acknowledging, and structuring classroom discourse. These insights underscore the importance of incorporating discourse relations and dialogue acts into AI-assisted education systems to enhance feedback and create more responsive learning environments. Our framework may prove helpful for providing human educator feedback, but also aiding in the development of AI agents that can effectively emulate the roles of both educators and students.
Study at a glance
- Question: How can analyzing classroom conversations from multiple perspectives provide more complete feedback for math teaching and tutoring?
- Setting and data: Two mathematics education dialogue datasets: TalkMoves (teaching) and SAGA22 (tutoring).
- Method: Computer-based dialogue analysis examining conversation patterns across multiple conversational frameworks (multi-perspective discourse analysis).
Why it matters
Effective feedback is essential for refining mathematics teaching. However, current dialogue analysis often fails to capture utterances serving multiple purposes and excludes many statements from domain-specific classifications, leading to their omission from pedagogical feedback.
Key findings
- In mathematics teaching and tutoring dialogues, statements lacking formal instructional moves serve crucial functions in guiding, acknowledging, and structuring conversations.
- Individual classroom utterances frequently serve multiple purposes at once, which single instructional tags cannot fully capture.
- Integrating dialogue acts and conversational relations reveals meaningful instructional discourse patterns across statements with and without specific teaching moves.
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