A Case Study of English Teacher Development in Hebei Normal University of Science and Technology
Bibliographic record
Abstract
Abstract: English teacher training is a very important part of teacher development in a university. This article reports a case study of English teacher development in Hebei Normal University of Science and Technology. A training program was given to improve the professional level of the young English Teachers. The result shows the school-based training improved teachers’ professional competence and received a good training effect. Several effective training patterns are proposed to enhance the English teacher development in universities. Key words: Teacher Development; English Teacher; Training Pattern; Teacher Education Resume: La formation de professeur d'Anglais est une part tres importante de developpement des professeurs au sein d’ une universite. Cet article rapporte une etude de cas du developpement de professeur d'Anglais a l'Universite Polytechnique de normale a Hebei. Un programme de formation a ete donne pour ameliorer le niveau professionnel des jeunes professeurs d'Anglais.Le resultat montre la competence professionnelle des professeurs ameliores par la formation scolaire et recoit un bon effet d'exercant. On propose plusieurs modeles efficaces de formation pour augmenter le developpement de professeur d'Anglais aux universites. Mots cles: Developpement de professeur; Professeur d’Anglais; Modele de formation; Formation de pedagogique
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".