Fourth International Workshop on Software Product Line Teaching (SPLTea 2019)
Bibliographic record
Abstract
Education has a key role to play for disseminating the constantly growing body of Software Product Line (SPL) knowledge. In a sense, every researcher in SPL should think about how to teach SPL. This workshop aims to explore and explain the current status and ongoing work on teaching SPLs at universities, colleges, and in industry (e.g., by consultants). This fourth edition will continue the effort made at SPLTea'14, SPLTea'15 and SPLTea'18. In particular we seek to better understand how to build a curriculum for teaching SPLs - a central issue as reported in surveys and as informally discussed at SPLTea'18. We expect several lightning talks that report on traditional questions like: what is the targeted audience? What is the place in the curriculum? What is the material (slides, tools, books, etc) used? As there is hardly a one-size-fits-all curriculum, the workshop aims to collectively identify commonality and variability when building SPL curriculums. As a concrete outcome, we expect to elaborate a variability model of SPL teaching that could be actuated to derive custom curriculum in various 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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.076 | 0.033 |
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".