Learning Management STEAM Model on Massive Open Online Courses Using Augmented Reality to Enhance Creativity and Innovation
Bibliographic record
Abstract
The purposes of this study were: 1) design Learning Management STEAM Model on Massive Open Online Courses Using Augmented Reality to enhance Creativity and Innovation, 2) suitability assessment of a Learning Management STEAM Model on Massive Open Online Courses Using Augmented Reality to enhance Creativity and Innovation. The research methodology was composed of two parts: the first part involved theories and research papers relating to massive open online courses, augmented reality, elements synthesis, and the design of a Learning Management STEAM Model on Massive Open Online Courses Using Augmented Reality to enhance Creativity and Innovation; the second part involved suitability assessment of this approach. Data were analyzed by using the statistic of the mathematic mean (x̄) and standard deviation (S.D.). The overall result with regard to the suitability of a Learning Management STEAM Model on Massive Open Online Courses Using Augmented Reality to enhance Creativity and Innovation by seven experts was assessed at a very high level, which can be applied to real situations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".