NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1427046
Record Type: Journal
Publication Date: 2024-Jun
Pages: 10
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0018-9359
EISSN: EISSN-1557-9638
Teaching Machine Learning as Part of Agile Software Engineering
Steve Chenoweth; Panagiotis K. Linos
IEEE Transactions on Education, v67 n3 p377-386 2024
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.
Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://bibliotheek.ehb.be:2578/xpl/RecentIssue.jsp?punumber=13
Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A