CoRe: A Comparative Study of Transformer Models for Context Recognition in Smart Classrooms

Abstract

With the increasing adoption of smart assistants, voice-enabled interactions have the potential to transform traditional classrooms into intelligent learning environments. A voice-enabled smart assistant can be designed to recognize context from student questions and brief conversations, enabling efficient query handling with minimal human intervention. This allows instructors to focus more on students who require personalized assistance, improving the overall learning experience. However, accurately identifying context remains a challenge due to the complexity and variability of student interactions. Therefore, in this paper, we present a comparative study of transformer models for context recognition (CoRe) in K-12 smart classrooms. We curated a custom dataset comprising short commands and conversations related to day-to-day classroom operations and trained multiple transformer models to identify the best-performing model for recognizing context. To ensure data quality and support data-driven decision-making, we performed topic modeling. Our evaluation results show that a transformer model enhanced with an attention mechanism outperforms existing transformer models while maintaining low computational costs, making it a viable solution for real-world smart classroom applications.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

10.1109/COMPSAC65507.2025.00028

Keywords

Attention mechanism, BERT, Natural language processing, Neural Network, SBERT, Topic Modeling, Transfer learning

Publication Date

1-1-2025

Journal Title

Proceedings 2025 IEEE 49th Annual Computers Software and Applications Conference Compsac 2025

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