A STEAM Learning with Digital Fabrication Laboratory on Cloud Computing Model to Enhance Creative Product
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research developed a model of steam learning with digital fabrication laboratory on cloud computing model to enhance creative product. The objectives of the study were: 1) To create the model, and 2) To evaluate a model. This research method was two parts. The first part about the design’s model had four subs: 1) to study and synthesize the relevant documents in this research such as steam, digital fabrication laboratory, cloud and creative product. 2) to develop a process in the model, 3) to present the process model with experts to get it approved to be able to hold in-depth interviews, and 4) to create the tools for assessing the model. The second part is referred to as model evaluation. The sample group has five experts who consist of Information Technology and Instructional Design. Then, this research uses means and standard deviations to analyze data. The process’s model has nine procedures in three components. The experts assed of the model overall found were a good level that the model could help learners in building creativity skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it