Application of Text Data Mining To STEM Curriculum Selection and Development
Application of Text Data Mining To STEM Curriculum Selection and Development
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文本数据挖掘在STEM课程选择与开发中的应用
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Roy Lowrance
中科院分区：
文献类型：
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作者：
A. Fortino;Qitong Zhong;WeiChieh Huang;Roy Lowrance
我们将来自机器学习的文本数据挖掘技术应用于纽约大学求职网站上发布的职位（工作）描述、美国劳工统计局（BLS）标准职位描述、课程描述和课程描述。我们的工作比较了词频逆文档频率（TD-IDF）潜在语义索引（LSI），并发现TD-IDF是首选在这个应用程序。我们使用TD-IDF来衡量我们的文档集合之间的一致性程度。然后，我们利用这些测量结果开发了新的方法来帮助学生和课程设计者回答这些问题：（1）对于学生来说，如果对特定的工作感兴趣，哪些学位和课程最相关;（2）对于学生来说，如果已经学习过的课程，哪些工作最有可能导致最初的面试;（3）对于课程设计者，学位课程与特定工作群体（例如，STEM工作）的一致性如何;（4）对于课程设计者，当前和拟议的学位在多大程度上解决了不同的工作机会。通过组合我们的Python和JMP代码，可以实现其他类似的应用。我们的工作可以通过提供算法的开源实现来扩展。
We applied text data mining techniques from machine learning to position (job) descriptions posted on NYU’s job search site, Bureau of Labor Statistics (BLS) standard U.S. job descriptions, course descriptions, and curricula descriptions. Our work compared Term Frequency-Inverse Document Frequency (TD-IDF) to Latent Semantic Indexing (LSI) and found that TD-IDF was preferred in this application. We used TD-IDF to measure the extent of coherence among the collections of our documents. We then leveraged those measurements to developed novel approaches to assist students and curricula designers in answering these questions: (1) for students, given an interest in specific jobs, which degrees and courses are most relevant; (2) for students, given courses that have been taken, which jobs are most likely to result in initial interviews; (3) for curricula designers, how aligned are degree programs with specific groups of jobs (for example, with STEM jobs); (4) for curricula designers, to what extent do current and proposed degrees address different job opportunities. Other similar applications are possible by composing our Python and JMP code. Our work could be extended by providing open source implementation of the algorithms.