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：学生对于课程的意见对教育者和行政管理者至关重要，无论课程类型或机构规模如何。对于大规模机构层面的评论或在线论坛中的海量反馈，手动阅读和分析开放式反馈变得不可行。本文收集并预处理了公开可用的大量课程评论，应用机器学习技术以深入理解学生情感与主题。具体而言，我们采用了当前自然语言处理（NLP）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，我们将这些技术与传统方法进行了对比。本研究展示了如何利用课程反馈，应用现代机器学习方法进行情感极性提取和基于主题的分类。在情感极性任务中，最佳模型为RoBERTa，达到95.5%准确率和84.7%的F1-宏平均值；而在主题分类任务中，SVM（支持向量机）以79.8%准确率和80.6%的F1-宏平均值成为最优分类器。此外，我们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。这些发现可为教育机构和课程提供方提供指导，帮助他们利用NLP模型分析自身课程反馈，以实现自我评估与改进。