{"title":"作为敏捷软件工程一部分的机器学习教学","authors":"Steve Chenoweth;Panagiotis K. Linos","doi":"10.1109/TE.2025.3572355","DOIUrl":null,"url":null,"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 one needs 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, statisticians, and software engineers.Intended Outcomes: The creation of a table detailing and matching industry challenges with course learning objectives, course topics, instructional units, and other related activities.Application Design: Course content was derived from interviewing industry professionals with related experience as well as surveying undergraduate computer science and engineering students. The proposed course style is designed to emulate real-world ML-based SE.Findings: Experienced IT professionals testify that the synergy between ML and agile SE is maturing and now becoming the standard practice. Thus, industry-derived content for a pilot undergraduate course has been successfully crafted at the intersection of SE and ML.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 4","pages":"322-335"},"PeriodicalIF":2.0000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Teaching Machine Learning as Part of Agile Software Engineering\",\"authors\":\"Steve Chenoweth;Panagiotis K. Linos\",\"doi\":\"10.1109/TE.2025.3572355\",\"DOIUrl\":null,\"url\":null,\"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 one needs 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, statisticians, and software engineers.Intended Outcomes: The creation of a table detailing and matching industry challenges with course learning objectives, course topics, instructional units, and other related activities.Application Design: Course content was derived from interviewing industry professionals with related experience as well as surveying undergraduate computer science and engineering students. The proposed course style is designed to emulate real-world ML-based SE.Findings: Experienced IT professionals testify that the synergy between ML and agile SE is maturing and now becoming the standard practice. Thus, industry-derived content for a pilot undergraduate course has been successfully crafted at the intersection of SE and ML.\",\"PeriodicalId\":55011,\"journal\":{\"name\":\"IEEE Transactions on Education\",\"volume\":\"68 4\",\"pages\":\"322-335\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Education\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11029686/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Education","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11029686/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
Teaching Machine Learning as Part of Agile Software Engineering
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 one needs 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, statisticians, and software engineers.Intended Outcomes: The creation of a table detailing and matching industry challenges with course learning objectives, course topics, instructional units, and other related activities.Application Design: Course content was derived from interviewing industry professionals with related experience as well as surveying undergraduate computer science and engineering students. The proposed course style is designed to emulate real-world ML-based SE.Findings: Experienced IT professionals testify that the synergy between ML and agile SE is maturing and now becoming the standard practice. Thus, industry-derived content for a pilot undergraduate course has been successfully crafted at the intersection of SE and ML.
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.