Effect of Backward Design With Virtual Learning Ecosystem to Enhance Design Thinking and Innovation Skills
Bibliographic record
Abstract
The backward design with virtual learning ecosystem aims to enhance design and innovative ideas as an application that simulates a virtual learning environment where students can interact with the simulated environment using their smartphones and digital goggles. This approach allows continuous, limitless self-learning based on cloud computing and social network, a guideline for the 21st century skill training that focuses on the learner, and enhance design and innovative ideas. This research aims were to (1) develop a backward design with virtual learning ecosystem to enhance design thinking and innovation skills, (2) study posttest learning achievement after application of the backward design with virtual learning ecosystem to enhance design thinking and innovation skill, (3) study design thinking skills assessment of students after using the backward design with virtual learning ecosystem to enhance design thinking and innovation skills, and (4) study posttest innovation of students after using backward design with virtual learning ecosystem to enhance design thinking and innovation skills. The sample group in this study is divided into 2 groups, i.e., (1) the seven experts in design and development of information technology media and technology digital media selected from various educational institutes, and (2) the 20 undergraduates from Digital Media Technology Program, Faculty of Industrial Technology, Muban Chombueng Rajabhat University currently enrolled in the DMT60711 Development of Mobile Application course.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".