Challenge-Based Hybrid Learning Model Using Virtual Board Games Platforms
Bibliographic record
Abstract
This study focuses on the development and evaluation of a challenge-based hybrid learning approach utilizing a virtual board games platform to enhance international standard student competency. Our research applies educational management theories to challenge-based learning, providing learners with diverse opportunities for engagement, investigation, and action. Through the integration of virtual board game platforms, students actively participate in learning activities aimed at elevating their skills to international standards. The study underscores the effectiveness of hybrid learning management, blending face-to-face and online elements, including board games as challenges, as the most suitable approach. Experts unanimously advocate for this approach, especially in advancing students’ capacities in creative problem-solving and critical thinking at an international level. Notably, scholars emphasize the significance of assessing students’ abilities to meet global benchmarks, positioning challenge-based learning as the predominant educational approach at the advanced level. This study provides empirical evidence supporting the efficacy of challenge-based hybrid learning using a virtual board game platform in fostering advanced competencies. And explores the integration of virtual board games in hybrid learning environments, offering a nuanced understanding of how educational management theories can be applied practically. Furthermore, it emphasizes the critical role of assessing students’ creative problem-solving and critical thinking abilities in competency evaluation. In conclusion, this study advances the discourse on hybrid learning methodologies, substantiates the effectiveness of challenge-based learning utilizing board games, and underscores the importance of evaluating students’ competencies at an international standard.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".