Facilitating Cross & Beyond Course Project-Based Software Engineering Learning Experiences
Bibliographic record
Abstract
Throughout each academic semester, software engineering students are often provided with opportunities to explore open-ended project-based activities. Within the confines of specific courses, many of these explorations have resulted in interesting and impactful, partially or fully engineered software solutions. However, after student-developed solutions are explored, tested, and delivered within a classroom setting it has been the author’s experience that they often don’t progress beyond the course in which students explored and created them in. The results of this are missed opportunities for innovation as well as missed opportunities for further creative and collaborative explorations. This work-in-progress explores the following question: what could a model, process, and/or framework look like that would enable software engineering educators to create a learning environment that facilitates continued exploration, collaboration, and iteration of project-based student work beyond individual courses? This paper will describe an exploratory hybrid framework called ORhiDeCy that the author has designed and has been exploring in his courses over the last several years. This paper describes ORhiDeCy, an example of its successful use in the author’s software engineering teaching practice, collaborator and student feedback, and the author’s reflections and ideas for continued explorations.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".