Utilizing Web Scraping and Natural Language Processing to Better Inform Pedagogical Practice
Utilizing Web Scraping and Natural Language Processing to Better Inform Pedagogical Practice
复制标题
利用网络抓取和自然语言处理更好地指导教学实践
DOI：
10.1109/fie44824.2020.9274270
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Monique S. Ross
中科院分区：
文献类型：
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作者：
Stephanie J. Lunn;Jia Zhu;Monique S. Ross
本研究全文描述了如何利用Web抓取和自然语言处理来回答计算机科学教育中的复杂问题。在这项工作中，我们应用连接主义的理论框架，并展示了如何网页抓取可以是有用的推断大量的数据从公开的网页池数据从更广泛的来源，并进一步在该领域的知识。此外，我们还讨论了如何使用自然语言处理来可靠地从文本数据中获取突出信息，以及它如何补充定性分析。为了在实践中说明这些技术，我们提供了一个具体的应用程序，在其中我们研究了计算机科学专业学生就业市场的当前趋势。本示例中收集的信息提供了教育考虑的其他领域，例如为学生提供Python编程语言和机器学习。此外，招聘信息明确要求申请人展示编程和测试技能。虽然编程可能已经教授，但测试被广泛认为是一种知识缺陷，这表明教育工作者应该考虑更加重视这一领域，以确保他们的学生为他们的职业生涯做好充分的准备，并能够转移所教的知识，以批判性地评估和调试自己的程序。
This research full paper describes how web scraping and natural language processing can be utilized to answer complex questions in computer science education. In this work, we apply connectivism as the theoretical framework, and demonstrate how web scraping can be useful for extrapolating large amounts of data from publicly available web pages to pool data from a wider array of sources and to further knowledge in the field. In addition, we discuss how natural language processing can be used to reliably obtain salient information from textual data, and how it can complement qualitative analysis. To illustrate these techniques in practice, we provide a specific application in which we examine the current trends in the job market for computer science students. The information gathered in this example provides additional areas for educational consideration, such as offering students Python programming language and machine learning. Also, the job postings delineate a clear need for applicants to exhibit programming and testing skills. Although programming may be taught already, testing is widely considered a knowledge deficiency, which suggests that educators should consider placing an increased emphasis on this area to ensure their students are adequately prepared for their career endeavors, and able to transfer the knowledge taught to critically assess and debug their own programs.
DOI：
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发表时间：
2000
期刊：
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影响因子：
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作者：
Dan Jurafsky;James H. Martin
通讯作者：
Dan Jurafsky;James H. Martin