University Teacher’s Knowledge, Personality and Teaching Effect: A Qualitative Study from Students’ Cognition Perspective
Bibliographic record
Abstract
Teaching effect is a core index for measuring the validity of teaching practices in universities. How to improve it in class? It is an important issue in educational reform. From students’ cognition perspective, the study analyzed students’ statements about the knowledge, personalities and behaviour of their teachers. Through the process of text analysis, the study summarized the common elements of teachers’ knowledge and personality as well as their impacts on teaching effects. A new theory, i.e. Intellectual Management for University Teacher (IMUT), was constructed. Results show that: At first, university students can definitely cognize and appraise their teachers’ knowledge and personalities; Second, an effective combination of knowledge and personality decides one teacher’s teaching effect, and; Finally, according to the feedback of students, the elements of teacher’s knowledge should include knowledge level and knowledge behaviour, and the elements of teacher’s personality could be summarized as personality trait and personalized behaviour. In order to improve teaching effects, university teachers are suggested to implement intellectual management, for realizing intellectual beauty through building a syncretic system which helps to develop knowledge and personality together.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".