高级检索|图表检索

图书情报工作 ›› 2022, Vol. 66 ›› Issue (2): 109-116.DOI: 10.13266/j.issn.0252-3116.2022.02.012

• 情报研究 • 上一篇    下一篇

大数据专业培养内容的主题分析及对图情档学科的启示

杨杰1, 赵星1,2   

  1. 1. 华东师范大学经济与管理学部信息管理系 上海 200062;
    2. 华东师范大学学术评价与促进研究中心 上海 200062
  • 收稿日期:2021-06-01 修回日期:2021-09-16 出版日期:2022-01-20 发布日期:2022-02-11
  • 作者简介:杨杰,本科生,E-mail:alexjieyang@outlook.com;赵星,经济与管理学部副主任,学术评价与促进研究中心主任,教授。
  • 基金资助:
    本文系国家自然科学基金面上项目“跨维度引文分析方法研究”（项目编号：71874056）研究成果之一。

Theme Analysis of the Training Contents of Big Data Subjects and the Enlightenment to the Library and Information Science

Yang Jie1, Zhao Xing1,2   

  1. 1. Department of Information Management, East China Normal University, Shanghai 200062;
    2. Institute for Academic Evaluation and Development, Shanghai 200062
  • Received:2021-06-01 Revised:2021-09-16 Online:2022-01-20 Published:2022-02-11

PDF

摘要： [目的/意义] 在大数据浪潮和“新文科”背景下，中国图情档学科的人才培养范式亟需改革。与此同时，大数据相关专业的建设方兴未艾，对于图情档学科的人才培养新范式建设具有借鉴意义。[方法/过程] 采用一种时序主题网络模型及计算方法；通过搜集、处理、统计、分析259所高等院校的大数据专业培养方案文本，在时间维度上进行主题挖掘，总结归纳数据科学课程的层次；分析图情档学科主干知识与大数据专业的联系，并给出适合图情档学科的数据科学课程建议。[结果/结论] 结果表明，所采用的时序主题网络模型方法能够较好地契合大数据专业的人才培养范式发展分析，可以成为研究学科主题的一种方法。此外，还给出面向图情档学科的数据科学类课程建议，可供图情档学科的人才培养参考。

关键词: 人才培养, 数据科学, 新文科, 时序主题网络模型

Abstract: [Purpose/significance] Under the background of the big data tide and the new liberal arts, there is an urgent need to reform and innovate the talent training mode of library and information science in China. The construction of the big data subjects is in the ascendant, which has a strong reference significance for the construction of a new paradigm of talent cultivation in library and information science. [Method/process] This paper innovatively proposed a new sequential topic network model and the calculation method. By collecting, processing, counting and analyzing the talent cultivation policies of the big data subjects in 259 universities, this paper conducted topic mining in the time dimension and summarized the levels of data science courses. Additionally, this paper analyzed the relevance between the subjects of the big data and the main knowledge of library and information science, and put forward suggestions for data science courses suitable for library and information science. [Result/conclusion] The sequential topic network model can better fit the analysis of talent cultivation paradigm development in the big data subjects, and it may also be a way to research the topic of a subject. Finally, this paper puts forward some suggestions for the courses of data science which is oriented to library and information science, and there is certain reference value for the development of talent cultivation in library and information science.

Key words: talent cultivation, data science, new liberal arts, sequential topic network model

中图分类号: