Bibliographic record
Abstract
Children's education is important for all countries.Teachers are in an important position in the preparation of children for the future.For this reason, teachers must be well trained.The purpose of the research is to determine the positive and negative characteristics of the teacher, the personal and professional characteristics of the teacher and the recommendations.For this purpose, he asked prospective teachers about the personal and professional positive and negative characteristics of his education life.The study group consists of 99 students from Dzce and Uak Universities.66 students studying at Dzce and Uak universities and 25 students who completed the form were taken in writing and interviewed with 8 class teachers.The research used qualitative research methods, document analysis and interview methods.Two different data types were collected and evaluated in the study.The data obtained in written form and obtained from the interviews were analyzed by content analysis.As a result of this research, the characteristics of qualified teachers; field knowledge, counseling, motivation, entertaining teaching and teaching were found.In addition, teachers who threaten students with notes, perform monotonous courses, and distinguish students were found to be inadequate.Teacher candidates should be informed about the characteristics of an effective teacher and it should be emphasized that this is important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.992 | 0.958 |
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; both teacher heads agree on what is shown here.
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".