The effectiveness of embedded values analysis modules in Computer Science education: An empirical study

IF 6.5 1区社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY

Big Data & Society Pub Date : 2022-08-10 DOI:10.1177/20539517231176230

Matthew Kopec, Meica Magnani, Vance Ricks, R. Torosyan, John Basl, Nicholas Miklaucic, Felix Muzny, R. Sandler, Christopher D. Wilson, Adam Wisniewski-Jensen, Cora Lundgren, Ryan Baylon, Kevin Mills, Marcy Wells

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引用次数: 3

Abstract

Embedding ethics modules within computer science courses has become a popular response to the growing recognition that computer science programs need to better equip their students to navigate the ethical dimensions of computing technologies such as artificial intelligence, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern University's program that embeds values analysis modules into computer science courses. The resulting data suggest that such modules have a positive effect on students’ moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they will likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought.

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嵌入式价值分析模块在计算机科学教育中的有效性：一项实证研究

在计算机科学课程中嵌入伦理模块已成为一种流行的回应，因为人们越来越认识到，计算机科学课程需要更好地让学生掌握人工智能、机器学习和大数据分析等计算技术的伦理维度。然而，这种方法的受欢迎程度已经超过了其积极成果的证据。为了帮助缩小这一差距，这项实证研究报告了东北大学在计算机科学课程中嵌入价值分析模块的项目的积极结果。由此产生的数据表明，这些模块对学生的道德态度有积极影响，学生离开模块时相信他们已经做好了更充分的准备，可以应对他们最终职业生涯中可能面临的道德层面。重要的是，这些成果是在一个没有哲学博士课程的机构中实现的，这表明这种策略可以被比许多人想象的更广泛的机构有效利用。

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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来源期刊

Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

10.90

自引率

10.60%

发文量

审稿时长

11 weeks

期刊介绍： Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.