调查变压器编码器架构对高推断话语的效应,以提高课堂观测评级的可靠性
Jinnie Shin1, Wallace N Pinto2, Bowen Wang2
1Research and Evaluation Methodology, College of Education, University of Florida, Gainesville, FL, USA. jinnie.shin@coe.ufl.edu.
Behavior research methods
|June 3, 2025
概括
该S-BERT双编码器模型显著改善了课堂话语分析,优于BERT和RoBERTa,用于分类学生与教师的互动,如高吸收和集中问题.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 教育技术的教育技术
- 教育中的人工智能
背景情况:
- 课堂话语分析对于理解学生与教师之间的互动至关重要.
- 变压器模型为分析复杂的教育对话提供了潜力.
- 现有的模型可能无法完全捕捉细微的话语元素.
研究的目的:
- 评估变压器编码器架构,用于分类高推理话语元素.
- 将S-BERT (双编码器) 与传统交叉编码模型 (BERT,RoBERTa) 的性能进行比较.
- 通过各种嵌入和聚合策略来优化S-BERT.
主要方法:
- 使用S-BERT,BERT和RoBERTa模型进行话语元素分类.
- 实验了各种嵌入策略,包括个人和交互特征 (点-产品,绝对距离).
- 测试了各种聚合方法和超参数设置 (批量大小8,学习速率2e-5).
主要成果:
- S-BERT实现了卓越的性能,F1得分高达0.826的高吸收和0.908的聚焦问题.
- 优化的S-BERT与特定的嵌入和聚合策略显著优于基线模型.
- 定制的矢量化策略和仔细的聚合选择是提高性能的关键.
结论:
- S-BERT双编码器架构对于课堂话语分析非常有效.
- 定制的嵌入和聚合策略对于在教育环境中最大限度地提高NLP模型性能至关重要.
- 这项研究通过改进课堂互动的分析来推进NLP在教育研究中的应用.
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