Teacher Behavior Unwanted According to Student’s Perceptions
Bibliographic record
Abstract
This study was conducted in the aim of revealing the misbehaviors of the teachers according to the perceptions of the students. In the study, semi-structured interview was done with 8th grade 45 students, 20 males and 25 females, from three secondary school determined through purposive sampling. The interviews were analyzed with content analysis, one of the qualitative research methods. All the data was coded and grouped as sub-theme, theme and main theme. As a result of the analysis, it was determined that misbehaviors of the teachers according to the perceptions of the students was collected under two main themes as relations and learning process. Misbehaviors of the teachers related to the relations was divided into 4 themes as being unfair, violence, communication barriers, characteristics. And violence was divided into two sub-themes as physical and psychological. Misbehaviors of the teachers related learning process was collected under three themes as boring classes, assessment and evaluation and classroom management.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".