The Virtual Learning Environment Model on Cloud using Hybrid Learning
Bibliographic record
Abstract
The objectives of this research are (1) to study and synthesise the conceptual framework of the virtual learning environment model on cloud using hybrid learning, (2) to develop the virtual learning environment model on cloud using hybrid learning, and (3) to study the results after using the virtual learning environment model on cloud using hybrid learning. The participants in this research include 10 experts from various institutions, all of whom are specialised in design and development of instruction models and instruction systems. The research tools herein consist of (1) the virtual learning environment model on cloud using hybrid learning, and (2) the evaluation form on the suitability of the virtual learning environment model on cloud using hybrid. According to the results of this research, it is found that (1) the overall suitability of the development of the virtual learning environment model on cloud using hybrid learning (overall elements) is at the highest level (Mean = 4.62, SD. = 0.49), and (2) the overall suitability of the development of the virtual learning environment model on cloud using hybrid learning is at the highest level (Mean = 4.66, SD. = 0.48).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".