Bibliographic record
Abstract
In this high-speed world in which everything is technologically driven, higher education also needs to incorporate technology into the scope of teaching pedagogy. Aligning educational games with the nursing curriculum is one way to address the need for technologically knowledgeable learners. Learning occurs in gaming environment is experimental, and constructive. Albeit, threading them in the nursing curriculum required in-depth knowledge about understanding brain involvement in this process. Nurse educators can thread gaming into the nursing content to ensure that learning occurs in a friendly environment. Learning games stimulates the release of dopamine in the midbrain, and the learning becomes part of long-term memory. The games must challenge and augment students’ interest so they get involved in the learning journey. The challenging environment, with clearly listed goals and ongoing feedback enhances learners’ interest and learning become part of their long-term memory. Gaming is an incomparable way of helping nursing students to learn actively and master learning skills. This literature review will discuss the phenomenon of gaming in education, the parts of brain that involved in educational games, scaffolding teaching and learning theories in designing educational games to improve and at last highlight the significance of gaming in nursing pedagogy. Use of games will open new horizon of possibilities to address various learning of different kinds of learners. This paper will act as a foundation to better comprehend the effective use of virtual world in academia.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".