Outcome-Based Approach to Teaching Students Comprehensive English in China: From “Golden Course” to “Golden Lessons”
Bibliographic record
Abstract
“Golden course”, which is against frivolous course, has become a hot topic in Chinese higher education as new requirements are imposed on classroom teaching efficiency. Under this background, this paper takes the course “Comprehensive English 3” as an example to discuss how to design “golden course” and“gold lessons”. To achieve this purpose, the author first constructs an outcome-based model of course design based on outcome-based education and briefly analyzes POA which is taken as a tool to implement beliefs of “golden lessons”. Guided by this model, development goals, students’ needs and course goals in the course are discussed. For the relationship, “golden course” is the basis of “golden lessons”, and a series of “golden lessons” is the realization of “golden course”. To realize “golden course”, the author designs one unit and discusses how to implement the beliefs by using one specific example. Either “golden course” or “golden lessons” is a new belief to all university teachers, the realization needs more research and this paper just provides implications for the teachers who would design “golden course” and implement its beliefs.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".