Micro science and technology fields requiring mathematically trained contributors: Topic modeling using journal paper abstracts
Micro science and technology fields requiring mathematically trained contributors: Topic modeling using journal paper abstracts
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需要受过数学训练的贡献者的微观科学和技术领域：使用期刊论文摘要进行主题建模
DOI：
10.1109/fie56618.2022.9962550
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ikegawa Takashi
中科院分区：
文献类型：
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作者：
山根由子;齊藤知範;齊藤知範;中迫由実;齊藤知範;Tomonori Saito;齊藤知範;菅澤貴之;桑畑洋一郎;菅澤貴之;菅澤貴之;桑畑洋一郎;Ikegawa Takashi
最近，一个明显的问题是，缺乏受过数学训练、能够将数学知识与其他类型的知识和技能相结合从而有助于创新的个人。找到帮助解决这个问题的线索需要：a）确定需要受过数学训练的贡献者的科学和技术领域（STF），以及B）确定为学生提供发展此类STF所需数学知识的机会的高等教育机构（HEI）。先前关于这个问题的工作已经表明，STF的粒度是粗的，例如在信息理论水平上。深入讨论教育实践，例如，基于项目的学习和工业实习计划，在数学高等院校需要在微观STF水平的分析。为了实现这样的分析，提出了一种用于发现隐藏在期刊论文摘要集合中的主题的方法。例如，使用Python中实现的潜在Dirichlet分配算法的主题建模使得能够发现在信息理论领域排名最高的三个数学高等教育机构所研究的主题。
A shortage of mathematically trained individuals who can integrate mathematical knowledge with other types of knowledge and skills and thereby contribute to innovation has recently become apparent. Finding clues to help in solving this problem requires a) identification of scientific and technology fields (STFs) that require mathematically trained contributors and b) identification of higher education institutions (HEIs) that provide students an opportunity to develop the mathematical knowledge needed in such STFs. Previous work on this problem has shown that the granularity of STFs is coarse, such as at the information theory level. An in-depth discussion of the educational practices, e.g., project-based learning and industrial internship programs, at mathematical HEIs requires analysis at the micro-STF level. To enable such analysis, a method is presented for discovering topics hidden in a collection of journal paper abstracts. For example, topic modeling using the latent Dirichlet allocation algorithm implemented in Python enabled the discovery of the topics covered studied at the three highest-ranked mathematical HEIs in the information theory field.