在线教育的增强文本聚类和情绪分析框架:BIF-DCN在计算机教育中的方法
Qingyun Zhang1, Yang Li1, Muhammad Sheraz Arshad Malik2
1Office of Academic Affairs, Shijiazhuang Vocational College of Technology and Information, Shijiazhuang, Hebei, China.
PeerJ. Computer science
|September 24, 2025
概括
一个新的深度学习框架,BERT和BTF-IDF集成框架与深度集群网络 (BIF-DCN),准确地分析了学生对在线教育的情绪. 这个工具有助于教育工作者了解学生的情绪,以改善教学和学习资源.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 自然语言处理自然语言处理.
背景情况:
- 了解学生的情绪对于有效的在线教学至关重要.
- 目前在教育环境中分析学生情绪的方法是不够的.
研究的目的:
- 开发一种新的深度学习框架,用于在线学习中准确分析学生情绪.
- 为教育工作者提供个性化教学和提高教学质量的工具.
主要方法:
- 使用来自变压器的双向编码器表示 (BERT) 来进行初始文本特征提取.
- 采用双级术语频率-反向文档频率 (BTF-IDF) 进行增强的特征表示.
- 应用了改进的深层嵌入式集群 (IDEC) 模型用于情绪分类和话题识别.
主要成果:
- 拟议的BERT和BTF-IDF集成框架与深度集群网络 (BIF-DCN) 与现有的基于IDEC和单一模型的方法相比,显示出更高的集群准确性.
- 该框架成功地在学生的评论中识别了基于情感的潜在主题.
- 在公开和自建数据集上实现了更高的准确性.
结论:
- 在教育平台上,BIF-DCN为深入的学生情绪分析提供了强大的解决方案.
- 该框架为优化教材和个性化教学提供了实用的见解.
- 通过更好地了解学生的需求,提高教学质量和学习者满意度.
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