The Imagineering Learning Model with Inquiry-Based Learning via Augmented Reality to Enhance Creative Products and Digital Empathy
Bibliographic record
Abstract
The objectives of this research are (1) to study and synthesise the conceptual framework of the imagineering learning model with inquiry-based learning via augmented reality to enhance creative products and digital empathy, (2) to develop the imagineering learning model with inquiry-based learning via augmented reality to enhance creative products and digital empathy, and (3) to study the results after using the imagineering learning model with inquiry-based learning via augmented reality to enhance creative products and digital empathy. The participants in this research include seven experts from various institutions, all of whom are specialised in the design and development of instruction models and instruction systems. The research tools consist of (1) the imagineering learning model with inquiry-based learning via augmented reality, and (2) the evaluation form on the suitability of the imagineering learning model with inquiry-based learning via augmented reality. According to the results of this research, it is found that (1) the conceptual framework of this research includes instruction system, imagineering learning, inquiry-based learning, augmented reality technology, creative products, and digital empathy, (2) the imagineering learning model with inquiry-based learning via augmented reality consist of four factors, i.e., input factor, learning process, output, and feedback, and (3) the study of the results after using the imagineering learning model by seven participants shows that 3.1) the overall suitability of the development of the imagineering learning model with inquiry-based learning via augmented reality (overall elements) is at the highest level (Mean = 4.69, SD. = 0.47), and 3.2) the overall suitability of the development of the imagineering learning model with inquiry-based learning via augmented reality is at the highest level (Mean = 4.70, SD. = 0.46).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".