University English Teachers’ Teaching Competencies in China: A Literature Review
Bibliographic record
Abstract
University English teachers in China comprise English as a Foreign Language (EFL) teachers for English-major students and College English (CE) teachers for non-English-major students in China. Teaching competencies of university English teachers have powerful effects on student learning and generally have several dimensions. However, there exist different taxonomies for the dimensions of teaching competencies. The purposes of this literature review are to first explore how researchers have defined teaching competency, secondly explore core dimensions of teaching competencies for the three teacher categories: university teachers, EFL teachers, and CE teachers, and finally investigate the differences in the core dimensions of teaching competencies between EFL teachers and CE teachers. Based on the core dimensions of each teacher category in the literature, the study concluded different three-dimension models of teaching competencies for university teachers, EFL teachers, and CE teachers. It found that CE teachers need to have an additional teaching competency in content knowledge related to their students’ disciplines besides all the required competencies for EFL teachers. EFL teachers are recommended to develop their competency in English language knowledge to meet English majors’ higher and intensive language needs. However, for CE teachers, although they don’t have to be as knowledgeable as EFL teachers in the English language, it would be more challenging for them since they are supposed to gain knowledge of students’ discipline which they might not have learned before.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".