Designing a MOOC Learning Environment embedded with Gamifications to Enhance Higher Order Thinking
Bibliographic record
Abstract
This study presents the design and development of a MOOC learning environment embedded with gamification elements aimed at enhancing higher-order thinking skills. The research adopts a design and development approach, utilising the ADDIE and ASSURE instructional design models. The study provides a comprehensive background and rationale for each component of the design process, highlighting the theoretical foundations that support the integration of gamification into the MOOC environment. Theories such as the Mechanics, Dynamics, and Emotions (MDE) framework for gamification are integrated into the learning activities to promote engagement and motivation. Additionally, elements of Online Collaborative Learning (OCL) are incorporated to facilitate interactive and social learning experiences, while the selected MOOCs reflect the principles of Connectivism, emphasising the networked nature of knowledge. A thorough analysis of these theories informs the design elements aimed at promoting cognitive development among learners. The proposed design for the MOOC and gamified learning environment is presented, demonstrating how these design principles can effectively foster higher-order thinking skills in an online learning context.
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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 | 0.003 |
| 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) | 0.001 | 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".