Teaching Machine Learning as Part of Agile Software Engineering

IF 2.1 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education Pub Date : 2023-12-21 DOI:10.1109/TE.2023.3337343

Steve Chenoweth;Panagiotis K. Linos

引用次数: 0

Abstract

Contribution: A novel undergraduate course design at the intersection of software engineering (SE) and machine learning (ML) based on industry-reported challenges. Background: ML professionals report that building ML systems is different enough that we need new knowledge about how to infuse ML into software production. For instance, various experts need to be deeply involved with these SE projects, such as business analysts, data scientists, and statisticians. Intended outcomes: The creation of a table detailing and matching industry challenges with course learning objectives, course topics, and related activities. Application design: Course content was derived from interviewing industry professionals with related experience as well as surveying undergraduate SE students. The proposed course style is designed to emulate real-world ML-based SE. Findings: Industry-derived content for a pilot undergraduate course has been successfully crafted at the intersection of SE and ML.

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将机器学习作为敏捷软件工程的一部分进行教学

贡献：基于行业报告的挑战，在软件工程（SE）和机器学习（ML）的交叉领域设计新颖的本科课程。背景：机器学习专业人士报告说，构建机器学习系统与以往不同，我们需要关于如何将机器学习融入软件生产的新知识。例如，各种专家需要深入参与这些 SE 项目，如业务分析师、数据科学家和统计学家。预期成果：创建一个表格，详细说明行业挑战并将其与课程学习目标、课程主题和相关活动相匹配。应用设计：课程内容来自于对具有相关经验的行业专业人士的访谈以及对本科 SE 学生的调查。建议的课程风格旨在模仿现实世界中基于 ML 的 SE。研究结果：在 SE 和 ML 的交叉点上，成功地为试点本科课程设计了源自行业的内容。

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

IEEE Transactions on Education 工程技术-工程：电子与电气

CiteScore

5.80

自引率

7.70%

发文量

审稿时长

1 months

期刊介绍： The IEEE Transactions on Education (ToE) publishes significant and original scholarly contributions to education in electrical and electronics engineering, computer engineering, computer science, and other fields within the scope of interest of IEEE. Contributions must address discovery, integration, and/or application of knowledge in education in these fields. Articles must support contributions and assertions with compelling evidence and provide explicit, transparent descriptions of the processes through which the evidence is collected, analyzed, and interpreted. While characteristics of compelling evidence cannot be described to address every conceivable situation, generally assessment of the work being reported must go beyond student self-report and attitudinal data.