Gamification-Based Engineering Design Process Instructional Model to Enhance Digital Innovation Creativity Skills for Upper Secondary School Students
Bibliographic record
Abstract
This study aims to develop and evaluate a gamification-based engineering design process (G-EDC) instructional model to enhance digital innovation creativity skills. The model integrates engineering design process stages with gamification techniques to create an engaging and problem-based learning environment. It emphasizes three key roles: Director (teacher), Disciple (student), and Debrief (reflection and refinement), along with eight stages of the engineering design process and core elements of gamification such as goals, points, rewards, and social interaction. The model was assessed by seven experts, including specialists in learning model development, educational evaluation, and gamification technology. The evaluation employed a 5-point Likert scale across six key components of the model. The results demonstrated a high level of appropriateness, with an overall average score of 4.73 (S.D. = 0.44). Specific strengths include the promotion of problem-solving and critical thinking (= 4.85), integration of digital tools (= 4.80), and alignment with learning objectives (= 4.80). These findings indicate statistically significant support for the model’s effectiveness in enhancing digital innovation creativity skills. The G-EDC model shows strong potential for flexible application across various educational contexts and contributes meaningfully to the development of 21st-century competencies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".