E-learning infrastructure for software engineering education : steps in ontology modeling for SWEBOK
Bibliographic record
Abstract
The Guide to the Software Engineering Body of Knowledge (SWEBOK) has been developed to represent an international consensus formed through broad public participation in the review process and is now close to final approval as ISO/IEC TR 19759. This guide constitutes an integrated structuring of a large set of software engineering concepts developed individually over the past forty years from a large number of distinct viewpoints. The absence of a recognized consensus on software engineering terminology has been a challenging task in building the SWEBOK Guide and in achieving this international consensus. This paper presents a first ontological approach to building domain-specific ontologies as a part of the Semantic Web, and shows how it can be used to build the SWEBOK ontology and to increase its internal consistency and clarity. Finally, new ideas on how a SWEBOK ontology can help in developing an e-learning system on software engineering
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".