A Study of Teaching Practices in Science under the Guidance of Embodied Cognition Theory
Bibliographic record
Abstract
With the increasing proliferation and integration of science and technology in contemporary society, there has been a substantial surge in both the quantity and quality of individuals' demand for science education. As a result, the previously overlooked issue of science teaching has garnered attention from all stakeholders. The implementation of the Science Curriculum Standards for Compulsory Education (2022 Edition) (hereinafter referred to as the 'New Science Curriculum Standards') has expedited reforms in science education and provided explicit guidance for enhancing science teaching. Concurrently, embodied cognition theory, as a modern cognitive theory of learning, holds significant relevance to the field of science. Reassessing students' cognitive patterns within this framework offers fresh insights into addressing current challenges encountered in primary school science teaching and exploring practical strategies that align with modernized teaching and learning requirements: i. Enhancing exploration through immersive and dynamic multiple teaching contexts; ii. Promoting school-based curricula to foster family practice and social projects; iii. Utilizing universally accessible and user-friendly technological learning products while exploring online-offline modes for embodied teaching and learning.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".