Student opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volumes of comments at institution level or online forums. In this paper, we collected and pre-processed a large number of course reviews publicly available online. We applied machine learning techniques with the goal to gain insight into student sentiments and topics. Specifically, we utilized current Natural Language Processing (NLP) techniques, such as word embeddings and deep neural networks, and state-of-the-art BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly optimized BERT approach) and XLNet (Generalized Auto-regression Pre-training). We performed extensive experimentation to compare these techniques versus traditional approaches. This comparative study demonstrates how to apply modern machine learning approaches for sentiment polarity extraction and topic-based classification utilizing course feedback. For sentiment polarity, the top model was RoBERTa with 95.5\% accuracy and 84.7\% F1-macro, while for topic classification, an SVM (Support Vector Machine) was the top classifier with 79.8\% accuracy and 80.6\% F1-macro. We also provided an in-depth exploration of the effect of certain hyperparameters on the model performance and discussed our observations. These findings can be used by institutions and course providers as a guide for analyzing their own course feedback using NLP models towards self-evaluation and improvement.

翻译：学生对课程的意见对于教育工作者和管理者至关重要，无论课程类型或机构性质如何。当评论数量庞大到机构层面或在线论坛级别时，对开放式的反馈进行人工阅读和分析变得不可行。本文收集并预处理了大量公开可用的在线课程评论，应用机器学习技术以洞察学生情感倾向与讨论主题。具体而言，我们采用了当前自然语言处理技术，如词嵌入和深度神经网络，以及先进的BERT（基于Transformer的双向编码器表示）、RoBERTa（鲁棒优化的BERT方法）和XLNet（广义自回归预训练）模型。通过大量实验，我们将其与传统方法进行了对比研究。这项比较研究展示了如何利用现代机器学习方法，基于课程反馈进行情感极性提取与主题分类。在情感极性任务中，RoBERTa模型表现最佳，准确率达95.5%，宏F1值为84.7%；而在主题分类任务中，支持向量机分类器以79.8%的准确率和80.6%的宏F1值名列前茅。我们还深入探讨了特定超参数对模型性能的影响，并讨论了相关观察结果。这些发现可供教育机构和课程提供者作为指南，利用自然语言处理模型分析自身课程反馈，以实现自我评估与改进。