On the Problems and Solutions of Professional Ethics Education of Normal Students from Educational Narrative Research
Bibliographic record
Abstract
Professional ethics is the core quality of teachers. Teachers' professional ethics education for normal students is a problem that society, universities and normal students must attach great importance. This paper uses narrative research to study the professional ethics education of outstanding normal students. Firstly, it expounds the connotation of narrative research. Secondly, in the form of interviews and narrations, the paper selects five normal students of 5 different majors from 4 different universities and named respectively A, B, C, D and E as the objects, to investigate the current situation of professional ethics education for college normal students, and to sort out some existing problems. Further, we delivered the questionnaires to broaden the scope of research. It is found that the way of professional ethics education for normal university students is single, mainly in class; the contents are boring, lacking of innovation; some normal students' consciousness of professional ethics is weak. So the effect of education needs to be improved urgently. The causes of these existing problems are also analyzed further. And some relative solutions have been proposed. It is expected to promote the development of professional ethics education for outstanding normal students.
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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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".