基于机器学习情感分析的总结性学生课程复习工具提升生命科学反馈效能
Summative Student Course Review Tool Based on Machine Learning Sentiment Analysis to Enhance Life Science Feedback Efficacy
摘要：
机器学习可以开发新的、补充的和授权的工具，这些工具可以扩展现有技术或发明新技术。在教育领域，存在支持通用学生课程复习格式的工具的空间，以组织和概括学生对他们所接触的教学实践的看法。通常情况下，学生的意见收集与一般评论部分，征求他们对他们的课程的感受，而没有投票的课程内容的细节。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，通过分析他们对课程的情感作为一个功能的语言/词汇来传达他们对一个类及其内容的意见。这一分析来自于他们对课后复习调查结束时遇到的一般性评论部分的答复。这种分析是通过Python、LaTeX和Google的自然语言API完成的，它允许将非结构化文本数据转换为通用和特定主题的子报告，以独特、新颖的方式传达学生的观点。
英文摘要：
Machine learning enables the development of new, supplemental, and empowering tools that can either expand existing technologies or invent new ones. In education, space exists for a tool that supports generic student course review formats to organize and recapitulate students' views on the pedagogical practices to which they are exposed. Often, student opinions are gathered with a general comment section that solicits their feelings towards their courses without polling specifics about course contents. Herein, we show a novel approach to summarizing and organizing students' opinions via analyzing their sentiment towards a course as a function of the language/vocabulary used to convey their opinions about a class and its contents. This analysis is derived from their responses to a general comment section encountered at the end of post-course review surveys. This analysis, accomplished with Python, LaTeX, and Google's Natural Language API, allows for the conversion of unstructured text data into both general and topic-specific sub-reports that convey students' views in a unique, novel way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
