GitKit: Learning Free and Open Source Collaboration in Context
Bibliographic record
Abstract
Modern version control tools and workflow practices are required skills for nearly all production software development, making them essential for students and in high demand among employers. Since these tools and processes were created for distributed, asynchronous collaboration on large scale projects, teaching them in an authentic context that makes clear their utility and design presents myriad challenges for both faculty and students. The GitKit is a snapshot of the FarmData2 Humanitarian Free and Open Source (HFOSS) project's artifacts (code, issues, documentation, etc.) frozen at a particular point in time and packaged with learning activities, an instructor guide, and a choice of containerized development environments. The GitKit thus provides students with the authentic context of a real-world project in which to learn and practice key Git and GitHub skills and workflows, while mitigating many of the challenges of doing so in an educational setting. The GitKit, including its learning activities and development environments are described in sufficient detail to encourage instructor adoption and feedback. A pilot study of student experiences with the GitKit is promising, suggesting that students gained an understanding of FOSS concepts and key skills, noticed automated guidance and feedback built into the development environment, and found it helpful in their learning. Future plans for the GitKit based on these surveys and instructor experiences with pilot uses are described along with plans for the development of HFOSS Kits for teaching and learning of other software development and aligned skills in authentic contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".