On Experiential Cultural Teaching Model in EFL College Classroom/DU MODELE D'ENSEIGNEMENT DE CULTURE EXPERIMENTAL DANS LA CLASSE D'ALE UNIVERSITAIRE
Bibliographic record
Abstract
Teaching culture in EFL classroom has long been debated and studied in foreign language teaching field. But the perfect combination of language and culture in a foreign language classroom is a noble aim and how the transition can be made from theoretical matters to the active, crowded, and sometimes noisy foreign language classroom is a completely different story. This paper will introduce a new cultural teaching model, named by Experiential cultural teaching model. This model is a merger of Kolb's model of experiential learning cycle and Moran's cultural experience, which emphasizes learners' experience and participation in cultural teaching in EFL classroom. Based on the previous achievements of this field, this paper will focus on answering the following questions: why is culture taught in EFL classroom? what culture should be taught in EFL classroom? what is experiential cultural teaching model? and how is the new model of experiential cultural teaching applied in EFL classroom? The last two questions will be investigated in a greater detail and some experiential cultural teaching techniques and strategies for EFL college classroom will be provided. It is hoped that the present paper will help contribute to a better understanding of culture and its importance in the foreign language classroom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".