STEAM Micro-learning Model based on Massive Open Online Courses with Augmented Reality Technology to enhance Creativity and Innovation
Bibliographic record
Abstract
This research addresses the pressing need for innovative educational frameworks that foster creativity and innovation in online learning environments. The study develops and validates a comprehensive model integrating STEAM education, micro-learning principles, and augmented reality (AR) technology within massive open online courses (MOOCs). Through systematic literature analysis and expert validation, the research demonstrates how these traditionally separate approaches can be combined to create engaging and effective learning experiences. The methodology employed a three-phase research and development approach: (1) systematic literature analysis using matrix mapping techniques to identify key components and relationships, (2) model development incorporating MOOC platform design, micro-learning structure, STEAM integration framework, and AR implementation, and (3) expert validation with ten qualified professionals in educational technology and instructional design. The evaluation instrument assessed model components, learning management stages, and measurement methods using a 5-point Likert scale. Results indicate strong expert endorsement of the proposed model across all evaluation criteria (overall mean = 4.72, SD = 0.46). Particularly high ratings were achieved for interdisciplinary connections (x̄ = 4.90) and STEAM media creation (x̄ = 4.90), demonstrating the model's effectiveness in maintaining meaningful cross-disciplinary integration in digital environments. The evaluation also validated the model's approach to assessing creativity and measuring innovation, providing a robust framework for evaluating complex educational outcomes. This research contributes to educational theory and practice by establishing a validated framework for integrating micro-learning principles with STEAM education and AR, advancing understanding of creativity development in online environments, and providing practical guidelines for implementing innovative educational approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".