Code Together, Apart: Teaching Asynchronous Team Development Workflows with GitHub.
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| Title: | Code Together, Apart: Teaching Asynchronous Team Development Workflows with GitHub. |
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| Authors: | Dana, Kareem, Abby Sen, Abraham, Mariam Joy, Jeen |
| Source: | Information Systems Education Journal; Mar2026, Vol. 24 Issue 2, p59-74, 16p |
| Subject Terms: | WORKFLOW, GROUPWARE (Computer software), INTERPERSONAL relations, STUDENT engagement, ONLINE education, TEACHING methods |
| Abstract: | Software development workflows in the information systems industry change quickly and are often different than what students experience in the classroom. This gap leaves many students underprepared for the engineering, analysis, or management jobs they seek. Asynchronous collaboration and tools like GitHub are increasingly prevalent in development teams. Traditional group projects, while beneficial, present persistent challenges, especially in online courses. This teaching case offers a practical assignment that bridges the classroom-industry gap and improves group work experience. Drawing on established pedagogical foundations, the assignment emphasizes cooperation—where students work independently toward a shared outcome—over collaboration, introduces students to real-world developer workflows with GitHub, and uses a narrative story to improve engagement. Students complete individual yet interdependent tasks within an existing codebase through GitHub. We share evidence showing that our assignment increased student confidence, self-efficacy, and satisfaction, particularly compared to traditional group projects. We conclude with several practical recommendations instructors can use to reduce group work frustration and build job-ready skills. [ABSTRACT FROM AUTHOR] |
| Copyright of Information Systems Education Journal is the property of Information Systems & Computing Academic Professionals (ISCAP) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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