The University of Ottawa's Software Engineering Program: Curriculum Design Issues for a New Subdiscipline
Bibliographic record
Abstract
The University of Ottawa has recently established a new undergraduate program in software engineering. In this paper we review how we resolved several issues that were raised during the development of the program. These key issues are: a) How should the program be made distinct from computer science and computer engineering programs? b) What should be the core topics in the program? c) What do we need to do to meet our objective of having the program accredited by the Canadian Engineering Accreditation Board? 1. Introduction Undergraduate programs entitled `Software Engineering' are appearing in many universities. In Europe and Australia, there have been programs for up to ten years (e.g. Cowling 1998, University of Melbourne 1998, Ford 1996b p. 49). More recently, North American universities have started to offer such programs (e.g. McMaster University 1998, Naveda 1997). This paper discusses the program recently approved at the University of Ottawa (University of Ottawa 1997) which ...
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".