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.
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.
How this classification was reachedexpand
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".