The Imagineering Learning Model with Inquiry-Based Learning via Augmented Reality to Enhance Creative Products and Digital Empathy
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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