Keeping Curriculum Relevant: Identifying Longitudinal Shifts in Computer Science Topics through Analysis of Q&A Communities

Habib Karbasian, A. Johri
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Abstract

Keeping up with new knowledge being produced in computing related domains is a difficult task given the pace of change in the field. Specifically, in domains that are undergoing a lot of innovation, such as Data Science or Artificial Intelligence, updating curricula is not easy. Yet, there is a need to be cognizant of new topics in order to create and update curricula and keep it relevant. In this paper we present an innovative approach to help educators keep a better track of changes in a domain and be able to map their curricula objectives to emerging topics and technologies. We leverage Q&A sites, Reddit and StackExchange, which provide a useful online platform for sharing of information and thereby generate a valuable corpus of knowledge. We use Data Science as a case study for our work and through a longitudinal analysis of these sites we identify popular topics and how they have changed over time. We believe innovations such as these are essential for improving computer science education and for bridging the workplace-school divide in teaching of newer topics. Our unique and innovative approach can be applied to other CS topics as well.
保持课程相关性:通过分析问答社区来识别计算机科学主题的纵向变化
考虑到该领域的变化速度,跟上计算相关领域产生的新知识是一项艰巨的任务。具体来说,在数据科学或人工智能等正在经历大量创新的领域,更新课程并不容易。然而,有必要认识到新的主题,以便创建和更新课程并保持其相关性。在本文中,我们提出了一种创新的方法来帮助教育工作者更好地跟踪一个领域的变化,并能够将他们的课程目标映射到新兴的主题和技术。我们利用问答网站,Reddit和StackExchange,它们提供了一个有用的在线信息共享平台,从而产生了一个有价值的知识语料库。我们使用数据科学作为我们工作的案例研究,通过对这些网站的纵向分析,我们确定了热门话题,以及它们如何随着时间的推移而变化。我们相信,这样的创新对于改善计算机科学教育,以及在新主题的教学中弥合工作场所和学校之间的鸿沟至关重要。我们独特和创新的方法也可以应用于其他CS主题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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