Functional Competency Development Model for Academic Personnel Based on International Professional Qualification Standards in Computing Field
Bibliographic record
Abstract
This research proposes a functional competency development model for academic personnel based on international professional qualification standards in computing field and examines the appropriateness of the model. Specifically, the model consists of three key components which are: 1) functional competency development model, 2) blended training system for academic personnel, and 3) test for computer science certifications. For functional competency development model, there are five sub-components which are: 1) objective, 2) qualities of academic personnel, 3) trainers, 4) content (curriculum), and 5) training plan. Next, the proposed blended training system consists of six different training modules which are: 1) pre-test, 2) face-to-face training, 3) e-Video lecture, 4) e-Web cooperation, 5) post-test, and 6) feedback. Lastly, for the third key component, there are 3 levels of certifications which are: 1) basic level, 2) specialist level, and 3) professional level, accordingly. The assessment result, based on opinions of 9 experts in the form of 5-point Likert scale, reveals that these three components and all their sub-components are rated as the highly appropriate ( x = 4.50).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".