A Methodology for Using GitLab for Software Engineering Learning Analytics
March 15, 2019 Β· Declared Dead Β· π IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies
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Authors
Julio CΓ©sar CortΓ©s RΓos, Kamilla Kopec-Harding, Sukru Eraslan, Christopher Page, Robert Haines, Caroline Jay, Suzanne M. Embury
arXiv ID
1903.06772
Category
cs.SE: Software Engineering
Cross-listed
cs.CY
Citations
8
Venue
IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies
Last Checked
4 months ago
Abstract
To bridge the digital skills gap, we need to train more people in Software Engineering techniques. This paper reports on a project exploring the way students solve tasks using collaborative development platforms and version control systems, such as GitLab, to find patterns and evaluation metrics that can be used to improve the course content and reflect on the most common issues the students are facing. In this paper, we explore Learning Analytics approaches that can be used with GitLab and similar tools, and discuss the challenges raised when applying those approaches in Software Engineering Education, with the objective of building a pipeline that supports the full Learning Analytics cycle, from data extraction to data analysis. We focus in particular on the data anonymisation step of the proposed pipeline to explore the available alternatives to satisfy the data protection requirements when handling personal information in academic environments for research purposes.
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