Evaluation of Teacher and Student Misbehaviours in Primary Schools from Prospective Teachers' Point of View
Bibliographic record
Abstract
The aim of this study was to evaluate teacher and student misbehaviours and the methods teachers use with regard tostudent misbehaviours in primary schools from prospective classroom teachers' point of view. The study usedphenomenological method, a qualitative research design, since thorough data collection was performed based onpersonal experiences. The study group consisted of junior (3rd grade) students registered for the 2017-2018 academicyear in the classroom teaching programme provided by a state university in the Central Anatolia region. 52prospective classroom teachers participated in the study, on a voluntary basis. While forming the study group, it wasintentionally preferred that prospective classroom teachers took the School Experience course. To collect data, asemi-structured interview form was prepared by the researcher. The form included 4 questions on demographiccharacteristics and 3 questions on the research topic. Data obtained from the study was analyzed by using descriptiveanalysis, which is widely used in qualitative studies. Certain conclusions were made based on the results obtainedfrom the research. Some of these conclusions can be stated as follows: 1. Some of the most common studentmisbehaviours in primary schools, according to prospective classroom teachers, are quarrelling with friends, talkingwithout taking permission, complaining, chatting among themselves, and wandering around the classroom. 2. Someof the most common teacher misbehaviours in primary schools, according to prospective classroom teachers, areextreme yelling, constant use of a particular method, discriminating students over one another, and cancelling playand physical activity lesson.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".