The Importance of Programming Paradigms to Manufacturing Engineering Graduates: A Case Study from International Islamic University Malaysia Graduates
Bibliographic record
Abstract
Digital computers that automate manufacturing process are an important aspect for any manufacturing engineers in the modern world. Selection of the programming paradigm (PP), as well as the programming language (PL) that supports it, is crucial to ensure the correct ideas are being used to automate the solution of the problem. In current Malaysian Higher institution practices, various PPs and PLs courses are offered to current undergraduate manufacturing majors. An online survey was deployed to experienced manufacturing engineers from various manufacturing specializations in the industry. Graduates from one of Malaysias public universities, International Islamic University Malaysia (IIUM) have been chosen for this particular study. From the survey, it has been found that almost 80% of the participants agreed that PPs are important for manufacturing graduates. It was found out that 90% of the participants were from intermediate (scale of 3) until poor (scale of 1) to express their ability to explain PPs if asked. Only about 10% are able to explain on PPs (scale of 4 and 5). The study concluded that majority of the manufacturing graduates from IIUM agreed that PP is an important subject to be taught in university. However, it was found the majority of the manufacturing graduates lack the knowledge and understanding of PPs and general PLs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".