How higher-education systems influence software engineering degree programs
Bibliographic record
Abstract
Over the last few years, many members of the software development community have advocated that software engineering should be underpinned as an independent profession. A prerequisite for this professionalization is the establishment of specific elements such as accreditation, certification, licensing, and an adequate education for future professionals. SE teaching has certainly undergone a series of changes over recent years. Traditionally, students gained SE knowledge mainly in postgraduate programs. A trend toward explicitly including this knowledge in undergraduate programs started as computer science programs began incorporating some SE knowledge as compulsory subjects. Furthermore, SE-specific programs have proliferated in several countries, such as the US, the UK, Australia, and Canada. Although these programs focus on SE knowledge, they might also offer electives related to advanced CS knowledge, depending on the university. After analyzing Australian, Canadian, European, and US HEI's we conclude that the primary cause of this difference in proliferation of SE programs in the organization of these countries' higher-education system. We also determine that these organizational differences don't generally affect the amount of SE knowledge that student's receive.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".