Principles of Curriculum Design and Construction Based on the Concepts of Educational Neuroscience
Bibliographic record
Abstract
With the emergence of a wealth of research-based information in the field of educational neuroscience, educators are now able to make more evidence-based decisions in the important area of curriculum design and construction. By viewing from the perspective of educational neuroscience, we can give a more meaningful and lasting purpose of leading to human development with enhanced consciousness or wisdom as the goal of a curriculum. We can better decide on the essential contents of a curriculum that is carried out within a limited time, using the emerging and validating information. Knowledge of educational neuroscience can also be used effectively for instructional design or conveying important messages to learners in the learning support material provided. Further, educators can be better directed in forming appropriate assessment so that learners are prepared for active and deep engagements in the teaching-learning process developing the skills of independence and discovery learning. Educational practitioners, as well as policy-makers, can also promote inclusive practices by directing, designing and constructing a curriculum appropriately especially taking into consideration the characteristics of right cerebral hemispheric oriented visual-spatial or gifted learners. Overall, education professionals can be benefited immensely to take more informed decisions in the process of curriculum design and construction by embracing emerging educational neuroscience principles.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".