Handbook of Research on Using Disruptive Methodologies and Game-Based Learning to Foster Transversal Skills
Bibliographic record
Abstract
As new technologies and professional profiles emerge, traditional education paradigms have to be adapted to new scenarios, creating favorable conditions for promoting transversal skills among students. Consequently, there is a growing demand for training in emergent skills to solve problems of different natures, distributive leadership competencies, empathy, ability to control emotions, etc. In this sense, one of the challenges that educators of all different educational levels and training contexts have to face is to foster these skills in their courses. To overcome these obstacles, innovative and disruptive methodologies, such as game-based learning activities like escape rooms, can be a great ally for teachers to work on transversal skills and specific knowledge at the same time. \n \nThe Handbook of Research on Using Disruptive Methodologies and Game-Based Learning to Foster Transversal Skills gathers knowledge, skills, abilities, and capabilities on innovative and disruptive methodologies that can be applied in all educational levels to foster transversal skills. This publication contains different contributions focused on the description of innovative educational methods, processes, and tools that can be adopted by teachers to promote transversal skills such as creativity, critical thinking, decision-making, and entrepreneurial skills. This book is ideal for teachers, instructional designers, educational software developers, academics, professionals, students, and researchers working at all levels in the educational field and provides valuable background information to professionals who aim to overcome traditional paradigm obstacles and meet student needs by means of innovative and disruptive methodologies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".