Computing Competency for Civil Engineering Graduates: Recent Updates and Developments in Saudi Arabia and the US
Bibliographic record
Abstract
This paper discusses recent updates and developments of computing-based courses in the civil engineering discipline. Competency in computing is one of the most important capabilities for university graduates to obtain given the rapid development of computer technology in professional work. Civil engineering is no exception. In fact, many contemporary civil engineering projects require a high degree of computing skills, ranging from performing basic office work to programming for decision support system application in controlling flood water gates to executing construction automation via digital printing technology. However, the curriculum content for computing in civil engineering has been developmentally stagnant in the past several decades. This could be partly due to learning outcomes for civil engineering graduates, which do not explicitly mention a certain degree of achievement with respect to computing skills. Several computing-based courses offered in various civil engineering programs across Saudi Arabia and the US were examined, and their contents were compared to recent survey results administered by the American Society of Civil Engineering Technical Committee on Computing and Information Technology. The discussion is extended by examining technical courses offered in the Civil Engineering Program in Prince Mohammad Bin Fahd University with respect to computing skills. The outcomes of this study are expected to give input and suggestions for future upgrades of computing-based courses offered within the civil engineering curriculum.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".