从词语到智慧：学生对话理解的语篇标注与基线模型
From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding
AI总结：
本文提出一个标注的教育对话数据集和基于预训练模型的基线模型，用于自动检测学生对话中的语篇特征，发现现有模型在该任务上表现欠佳，提示未来研究方向。
摘要：
识别学生对话中的语篇特征对于教育研究者识别导致学生构建知识而非仅仅完成任务的课程和教学变量至关重要。对学生成语进行手动分析以识别这些语篇特征是耗时且劳动密集的，这限制了研究的规模和范围。利用自然语言处理（NLP）技术可以促进这些语篇特征的自动检测，为教育研究者提供可扩展和数据驱动的洞察。然而，现有专注于对话中语篇的NLP研究很少涉及教育数据。在本工作中，我们通过引入一个标注了学生对话的教育数据集来填补这一空白，该数据集包含知识构建和任务生产语篇特征。我们还建立了基线模型，利用预训练的大型语言模型GPT-3.5和Llama-3.1，自动预测对话中每个回合的语篇属性。实验结果表明，这些最先进的模型在该任务上表现欠佳，表明了未来研究的潜力。
英文摘要：
Identifying discourse features in student conversations is quite important for educational researchers to recognize the curricular and pedagogical variables that cause students to engage in constructing knowledge rather than merely completing tasks. The manual analysis of student conversations to identify these discourse features is time-consuming and labor-intensive, which limits the scale and scope of studies. Leveraging natural language processing (NLP) techniques can facilitate the automatic detection of these discourse features, offering educational researchers scalable and data-driven insights. However, existing studies in NLP that focus on discourse in dialogue rarely address educational data. In this work, we address this gap by introducing an annotated educational dialogue dataset of student conversations featuring knowledge construction and task production discourse. We also establish baseline models for automatically predicting these discourse properties for each turn of talk within conversations, using pre-trained large language models GPT-3.5 and Llama-3.1. Experimental results indicate that these state-of-the-art models perform suboptimally on this task, indicating the potential for future research.
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。
