A STEAM Learning Ecosystem on Gamification System to Promote Innovators
Bibliographic record
Abstract
This study aimed to synthesize and evaluate a STEAM Learning Ecosystem on Gamification System to Promote Innovators. The research was conducted in three phases: (1) synthesis, (2) development of the learning ecosystem, and (3) expert evaluation. The resulting ecosystem comprises three main elements: (1) STEAM Learning Ecosystem (Instructor, Learner, Investigate, Discover, Create, and Reflect), (2) Gamification Process (Goals, Rules, Reinforcement, Time Feedback, Competition/Cooperation, and Feedback), and (3) Innovation Skills (Creativity, Imagination, Inventiveness, Idea Generation, Novel Approaches and Problem-Solving). Nine experts evaluated the ecosystem's suitability using a questionnaire. The results showed the highest level of suitability (mean±SD=4.60±0.49) across five aspects: objectives, design principles, elements, learning process, and ease of understanding. This research contributes to STEAM education by providing a comprehensive framework integrating gamification strategies to foster innovation skills. The proposed ecosystem offers a structured approach for educators to design engaging and effective STEAM learning experiences that promote innovative thinking. Further research could explore this ecosystem's practical implementation and impact in various educational settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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".