源代码中的人类语言:本地化指令的自动翻译

C. Piech, Sami Abu-El-Haija
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引用次数: 3

摘要

计算机科学教育承诺在世界范围内开放访问,但访问在很大程度上取决于你所说的人类语言。当年轻的学生学习计算机科学时,假设他们应该事先学习英语是不合适的。为此,我们提出了CodeInternational,这是第一个在人类语言之间翻译代码的工具。为了发展非英语代码理论,并为我们的翻译决策提供依据,我们对GitHub上的公共代码库进行了研究。据我们所知,这项研究是第一个用人类语言编写代码的研究,涵盖了290万个Java存储库。为了演示CodeInternational的教育实用程序,我们构建了一个流行的英语卡雷尔阅读器的交互式版本,并将其翻译成100种口语。我们的翻译已经在世界各地的课堂上使用,代表了一个重要的开放cs教育问题的第一步。
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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Human Languages in Source Code: Auto-Translation for Localized Instruction
Computer science education has promised open access around the world, but access is largely determined by what human language you speak. As younger students learn computer science it is less appropriate to assume that they should learn English beforehand. To that end, we present CodeInternational, the first tool to translate code between human languages. To develop a theory of non-English code, and inform our translation decisions, we conduct a study of public code repositories on GitHub. The study is to the best of our knowledge the first on human-language in code and covers 2.9 million Java repositories. To demonstrate CodeInternational's educational utility, we build an interactive version of the popular English-language Karel reader and translate it into 100 spoken languages. Our translations have already been used in classrooms around the world, and represent a first step in an important open CS-education problem.
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Trust, Sustainability and [email protected] L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1 - 3, 2022 L@S'21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021 Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior
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