Neuropedagogy for Improving the Educational Process in Universities
Bibliographic record
Abstract
Objective: This study aims to investigate the problems that neuropedagogy can solve in the context of the educational process. Background: Research in neuropedagogy has been gaining popularity in recent years. This has been driven by advances in technology and the social sciences. Every year more and more scientists approach the subject of neuropedagogy to modernise the educational system and improve the educational process. Method: The main research methods in this scientific article are the comparative method, methods of analysis and data synthesis, the historical method, as well as the theoretical analysis of scientific literature related to the subject matter. Results: The study explored the concept of neuropedagogy and the principles on which it is based. Practical recommendations for teachers of Kazakhstan have been developed. Scientific articles on the subject matter are analysed, the degree of research and the effectiveness of the implementation of neuropedagogy in the educational process is assessed. Conclusion: It was concluded that the use of neuropedagogy would provide an opportunity to improve and modernise the education system.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".