PedagoSense: 一种基于土壤学的LLM系统，用于学习对话中的教学策略检测与上下文响应生成
PedagoSense: A Pedology Grounded LLM System for Pedagogical Strategy Detection and Contextual Response Generation in Learning Dialogues
AI总结：
PedagoSense通过结合两阶段策略分类器和大语言模型，实现了学习对话中教学策略的检测与上下文响应生成，提升教育技术的适应性。
摘要：
本文针对通过对话学习提高交互质量的挑战，通过检测和推荐有效的教学策略来改进辅导学生对话。我们引入了PedagoSense，一种基于土壤学的系统，结合了两阶段策略分类器和大语言模型生成。该系统首先使用二分类器检测是否存在教学策略，然后进行细粒度分类以确定具体策略。同时，它从对话上下文中推荐合适的策略，并使用LLM生成与该策略一致的响应。我们在人工标注的辅导学生对话上进行了评估，并补充了额外的非教学对话用于二分类任务。结果表明，教学策略检测具有高性能，使用数据增强时获得了一致的提升，而分析突显了细粒度类别仍具挑战性。总体而言，PedagoSense将教学理论与基于LLM的响应生成相结合，为更适应的教育技术提供了桥梁。
英文摘要：
This paper addresses the challenge of improving interaction quality in dialogue based learning by detecting and recommending effective pedagogical strategies in tutor student conversations. We introduce PedagoSense, a pedology grounded system that combines a two stage strategy classifier with large language model generation. The system first detects whether a pedagogical strategy is present using a binary classifier, then performs fine grained classification to identify the specific strategy. In parallel, it recommends an appropriate strategy from the dialogue context and uses an LLM to generate a response aligned with that strategy. We evaluate on human annotated tutor student dialogues, augmented with additional non pedagogical conversations for the binary task. Results show high performance for pedagogical strategy detection and consistent gains when using data augmentation, while analysis highlights where fine grained classes remain challenging. Overall, PedagoSense bridges pedagogical theory and practical LLM based response generation for more adaptive educational technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
