Thapar Titan/s : Fine-Tuning Pretrained Language Models with Contextual Augmentation for Mistake Identification in Tutor–Student Dialogues

Harsh Dadwal, Sparsh Rastogi, Jatin Bedi


Abstract
This paper presents Thapar Titan/s’ submission to the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors. The shared task consists of five subtasks; our team ranked 18th in Mistake Identification, 15th in Mistake Location, and 18th in Actionability. However, in this paper, we focus exclusively on presenting results for Task 1: Mistake Identification, which evaluates a system’s ability to detect student mistakes.Our approach employs contextual data augmentation using a RoBERTa based masked language model to mitigate class imbalance, supplemented by oversampling and weighted loss training. Subsequently, we fine-tune three separate classifiers: RoBERTa, BERT, and DeBERTa for three-way classification aligned with task-specific annotation schemas. This modular and scalable pipeline enables a comprehensive evaluation of tutor feedback quality in educational dialogues.
Anthology ID:
2025.bea-1.103
Volume:
Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Ekaterina Kochmar, Bashar Alhafni, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
Venues:
BEA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1278–1282
Language:
URL:
https://preview.aclanthology.org/landing_page/2025.bea-1.103/
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Bibkey:
Cite (ACL):
Harsh Dadwal, Sparsh Rastogi, and Jatin Bedi. 2025. Thapar Titan/s : Fine-Tuning Pretrained Language Models with Contextual Augmentation for Mistake Identification in Tutor–Student Dialogues. In Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), pages 1278–1282, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
Thapar Titan/s : Fine-Tuning Pretrained Language Models with Contextual Augmentation for Mistake Identification in Tutor–Student Dialogues (Dadwal et al., BEA 2025)
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PDF:
https://preview.aclanthology.org/landing_page/2025.bea-1.103.pdf