Applying Embodied Cognition: Exploring a New College English Teaching Paradigm of Open Universities
Bibliographic record
Abstract
Teaching paradigm of higher education not only directly represents the overall appearance and practical level of teaching, but also reflects the quality of qualified personnel cultivation in essence. The innovation and optimization of teaching paradigm, thus, has become the logical premise and necessary path for open universities to fulfill its mission of high-quality development. However, the existing college English teaching paradigm of open universities is still in the state of disembodied cognition paradigm, which has become a hindrance to deepen teaching reform, and leads to the quality problems unsolved in a long run. In view of this, we should apply the latest cognitive science as guidance and the three aspects of paradigm as the analytical framework to remold the existing paradigm from the perspective of the metaphysical aspect, the sociological aspect, and the constructive aspect respectively. In the metaphysical aspect of paradigm, adopt embodied cognition as the epistemology of English teaching. In the sociological aspect of paradigm, insist principles of embodiment, situation, enactment and dynamic on the methodology of English teaching. In the constructive aspect of paradigm, incorporate embodied cognition in current main teaching modes, i.e., “Intelligence＋” teaching, blended teaching, multimodal teaching. The above-mentioned three aspects serve as a systematic and organic whole in striving to improve teaching quality, and ultimately facilitate a shift from the view of “ the disembodied” to “the embodied”.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".