Applied in Organic Chemistry: Pre-service Teachers Training through Situational Simulation Teaching Method
Bibliographic record
Abstract
Situational simulation teaching method (SST mentioned below) is a mature teaching method that has been applied. It has been widely used in foreign language, law, management, clinical and other fields, and has been proved to have good teaching effect. The quality of teachers is the key to improve the international competitiveness of China's education system. With the growth of China's population and the reform and development of education, the training of pre-service teachers has become a public concern. According to the existing research, most of the pre-service teachers have good academic and moral qualities, but there are still deficiencies in teaching ability, management ability and communication ability. .In view of this phenomenon, this paper puts forward a scheme of training chemistry pre-service teachers by using SST method through the way of organic chemistry teaching reform. The results show that SST method can improve students' learning quality and cultivate students' comprehensive abilities (including pre-service teachers' professional skills).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".