Report from the 2nd International Workshop on Software Engineering Course Projects (SWECP 2005)
Bibliographic record
Abstract
This paper reports on the activities and results from the 2nd International Workshop on Software Engineering Course Projects (SWECP 2005), which was held on October 18, 2005 in Toronto, Canada. Creating software engineering course projects for undergraduate students is a challenging task. The instructor must carefully balance the conflicting goals of academic rigor and industrial relevance. Some of the fundamental characteristics of software engineering projects (e.g., team-based, large-scale, long-lived) are difficult to realize within the constraints of a university course in a single semester. This is particularly true when dealing with young students who may lack the real-world experience needed to appreciate some of the more subtle aspects of software engineering. This workshop explored how educators and industry can work together to develop a more rewarding educational experience for all stakeholders involved. Several key themes emerged from the workshop, including the importance of forming teams that are fair and balanced, the challenges in selecting a project that engages the students and meets the goals of the course, and the need for knowledge transfer amongst instructors.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.022 |
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".