用情感分析读懂在线学习反馈，提升师生沟通质量。
Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale
- 在LMS中融合深度神经网络，结合词嵌入与注意力机制
- 模型比逻辑回归基线更准确识别学生反馈情绪
- 适合教育科技开发者与在线教学研究者参考
新冠疫情后，教育模式从线下转向线上平台，导致师生间非语言交流缺失，依赖口头反馈削弱了教学效果。本文将情感分析引入学习管理系统（LMS），通过数据准备、特征选择及构建包含词嵌入、LSTM和注意力机制的深度神经网络模型，对比逻辑回归基线，评估其在理解学生反馈情感语境中的有效性。旨在缩小在线教学环境中师生沟通鸿沟，提供学生反馈的情绪洞察，从而提升在线教育质量。
原文摘要 · Abstract (English)
During the wake of the Covid-19 pandemic, the educational paradigm has experienced a major change from in person learning traditional to online platforms. The change of learning convention has impacted the teacher-student especially in non-verbal communication. The absent of non-verbal communication has led to a reliance on verbal feedback which diminished the efficacy of the educational experience. This paper explores the integration of sentiment analysis into learning management systems (LMS) to bridge the student-teacher's gap by offering an alternative approach to interpreting student feedback beyond its verbal context. The research involves data preparation, feature selection, and the development of a deep neural network model encompassing word embedding, LSTM, and attention mechanisms. This model is compared against a logistic regression baseline to evaluate its efficacy in understanding student feedback. The study aims to bridge the communication gap between instructors and students in online learning environments, offering insights into the emotional context of student feedback and ultimately improving the quality of online education.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。