On Guidelines for College English Teaching and Challenges for College English Teachers
Bibliographic record
Abstract
This article performs an exploratory study of the newly formulated Guidelines for College English Teaching (Draft Exposure)(2015)(Guidelines), aiming at exploring how different the latest Guidelines is from the previous ones, what challenges it brings to teachers and how these challenges can be countered. Therefore, comparisons are made among six syllabi to illustrate the developments college English has achieved and its existent problems as well. To address these problems, Guidelines (2015) is issued with three new features: the integration of instrumentality and humanity, the introduction of intercultural education and the system of multiple curriculum for multiple teaching objectives. Its issuing poses tremendous challenges to college English teachers, of which increasing demands on college English teachers' professional expertise, skillful employment of information technology and academic performance stand out. To help counter these challenges and difficulties, suggestions are made from three levels with the hope that improved teachers' quality can facilitate the implementation of Guidelines (2015) and guarantee the potential achievement of its expected objectives. These suggestions are: life-long learning and self-directed development consciousness, the establishment of teacher education system by faculties and universities and continued governmental support in and favorable policies to college English teacher education.
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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 | 0.008 | 0.024 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".