Student Experiences with GitHub and Stack Overflow: An Exploratory Study
Bibliographic record
Abstract
Programmers who want to improve their skills in software development rely heavily on developer social platforms such as GitHub and Stack Overflow to enhance their learning. Stack Overflow provides answers to questions they have about languages or library skills they wish to acquire, while contributing to open-source projects hosted on sites like GitHub gives them valuable experience. Students also use these platforms during their education: most will rely heavily on Stack Overflow at some point in their schooling, while many can benefit from contributing to GitHub projects to build their expertise and professional portfolios. We already know from previous research that developers face barriers participating on these platforms, so we may expect that at least some students will experience similar barriers and possibly even bigger challenges. This paper describes a semi-structured interview study with university students to explore how they use the GitHub and Stack Overflow platforms. We identify the barriers they face and benefits they report from using these tools. We conclude with some preliminary recommendations on how to reduce the hurdles students may face with these and other developer social platforms, and suggest future work to mitigate these roadblocks.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".