Mapping Mindset about Gamification: Teaching Learning Perspective in UAE Education System and Indian Education System
Bibliographic record
Abstract
One can observe the dramatic change in the ongoing global pandemic impacts, and the speed of advancement in the educational learning system, particularly in virtual teaching learning process, has been extremely quick.Teachers' use of technology to deliver instruction to students via a variety of platforms has a significant impact on how well those students learn.A variety of factors influence how well students learn and how well teachers teach, including how well they use the most effective teaching technique.Teachers' and students' perspectives on instructional strategies should take precedence.Empirical study will be undertaken to demonstrate that there are tactics and approaches, such as Gamification, that teachers may use to improve their teaching.The proposed study looked into teachers' reported usage and implementation of these instructional tactics in their classrooms in different schools in the United Arab Emirates (UAE) & India.The parents also adopted the strategies for digital transformation of their children.Participants in the research included teachers from schools in the United Arab Emirates and India.Motivation is to find and reveal that teachers are employing ICT approaches such as Gamification and are also extremely aware of and comfortable with new teaching methodologies.Other findings show that teachers in both countries agree on the necessity of using digital tools to improve the learning outcomes of their students.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".