Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis
Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis
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DOI：
10.1021/acs.jchemed.3c00258
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发表时间：
2023-09
影响因子：
3
通讯作者：
Benjamin B. Hoar;Roshini Ramachandran;M. Levis-Fitzgerald;Erin M. Sparck;Ke Wu;Chong Liu
中科院分区：
文献类型：
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作者：
Benjamin B. Hoar;Roshini Ramachandran;M. Levis-Fitzgerald;Erin M. Sparck;Ke Wu;Chong Liu
在教育方面，存在着通过组织和概括学生对他们所接触的教学实践的看法来评价通用学生课程评价格式的工具的空间。通常情况下，学生对课程的意见是通过一般性评论部分收集的，该部分不征求有关特定课程内容的反馈。在这里，我们展示了一种新的方法来总结和组织学生的意见，作为他们的课程评估中使用的语言的函数，特别是专注于开发软件，输出可操作的，具体的反馈，在大招生STEM背景下的课程组件。我们的方法增强了现有的课程复习格式，这些格式严重依赖于非结构化的文本数据，并使用由Python，LaTeX和Google的自然语言API构建的工具。其结果是定量的，总结性的情绪分析报告，具有一般和特定组件的部分，旨在解决教育工作者在教授大型物理科学课程时面临的一些挑战。
In education, space exists for a tool that valorizes generic student course evaluation formats by organizing and recapitulating students’ views on the pedagogical practices to which they are exposed. Often, student opinions about a course are gathered using a general comment section that does not solicit feedback concerning specific course components. Herein, we show a novel approach to summarizing and organizing students’ opinions as a function of the language used in their course evaluations, specifically focusing on developing software that outputs actionable, specific feedback about course components in large-enrollment STEM contexts. Our approach augments existing course review formats, which rely heavily on unstructured text data, with a tool built from Python, LaTeX, and Google’s Natural Language API. The result is quantitative, summative sentiment analysis reports that have general and component-specific sections, aiming to address some of the challenges faced by educators when teaching large physical science courses.