Changes in Teaching Activities of General English and Evaluation Criteria Proposals at Sai Gon University
Bibliographic record
Abstract
To explore the positive changes in teaching activities for General or non-specialist English and proposals for evaluation criteria at Sai Gon University, a methodological approach involving listing, describing, and synthesizing was employed. This approach aimed to identify the changes in non-specialist English teaching activities in the context of modern technology and the proposals for evaluation criteria that enhance the capabilities of both lecturers and students at Sai Gon University.The purpose of the article is to clarify the effects of changing the results of non-specialist English learning and teaching at Sai Gon University and evaluation criteria in lecturers’ teaching capacity and students’ language learning ability at Sai Gon University.The modifications in teaching tasks and assessment standards, as well as the results of the research through tables of changes in English skills outcomes in non-English majors at Sai Gon University and changes in the lecturer’s teaching activities as well as student learning activities in non-specialized English teaching activities at Sai Gon University.The article serves as a valuable reference for researchers investigating changes in educational activities and evaluation criteria within the field of language teaching.
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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 | 0.070 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".