Exploring United Arab Emirates School Teachers’ Perceptions, Motivation and Benefits of Game-Based Teaching and Learning Environments
Bibliographic record
Abstract
Recent research specifies that game-based learning effectively engages students in classroom activities. Although there has been a prominent upsurge in game-based teaching and learning, this area has not received significant attention within the context of Gulf nations. This study investigates the perceptions, motivations, and benefits of game-based teaching-learning among school teachers, aiming to enhance the interactive learning environment. This study used a quantitative method and survey data from 505 school teachers who were actively teaching around the United Arab Emirates to reach its objectives. The majority of teachers believed that using games as a teaching tool in the classroom was a good idea. The study revealed the potential for game-based learning in the classroom and identified challenges, such as the need for rigorous game-based instructional design. We examined game-based teaching and learning’s role in contributing to interactive learning environments and its apparent benefits of improving teamwork and lowering stress among teachers. Des recherches récentes précisent que l'apprentissage par le jeu permet d'impliquer efficacement les étudiants dans les activités de la classe. Bien que l'enseignement et l'apprentissage par le jeu aient connu un essor important, ce domaine n'a pas reçu une attention particulière dans le contexte des pays du Golfe. Cette étude examine les perceptions, les motivations et les avantages de l'enseignement et de l'apprentissage par le jeu chez les enseignants, dans le but d'améliorer l'environnement d'apprentissage interactif. Pour atteindre ses objectifs, cette étude a utilisé une méthode quantitative et des données d'enquête provenant de 505 enseignants dans les écoles de la région des Émirats arabes unis. La majorité des enseignants ont estimé que l'utilisation des jeux comme outil d'enseignement en classe était une bonne idée. L'étude a révélé le potentiel de l'apprentissage par le jeu en classe et a identifié des défis, tels que la nécessité d'une conception pédagogique rigoureuse basée sur le jeu. Nous avons examiné le rôle de l'enseignement et de l'apprentissage par le jeu dans la création d'environnements d'apprentissage interactifs, ainsi que ses avantages apparents en termes d'amélioration du travail d'équipe et de réduction du stress chez les enseignants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".