Teachers’ and Students’ Opinions About Students’ Attention Problems During the Lesson
Bibliographic record
Abstract
This research which investigates teachers’ and students’ opinions about students’ attention problems during the lesson is a descriptive study in the survey model. 432 teachers and 1023 students from secondary schools in the central districts of Adana voluntarily participated in the study. The research data were collected with a Written Interview Form developed by the researchers and a descriptive content analysis was used for data analysis. As a result of the research, it was observed that the teachers perceived the attention problems that the students experienced during the course mostly as a problem arising from the students themselves while the students associate this problem not only with themselves, but also with other students, teachers and the environment. According to the results, teachers as well as students easily noticed the psychological characteristics, the behaviors they exhibited and their low academic performance, but the teachers evaluate this situation more as disciplinary problems. The solution suggestions of the teachers who kept the attention problems of the students out of their own sphere and their teaching practices were that passing exams should be harder and discipline regulations should change to facilitate punishment. The students stated that teachers should show more interest towards the students, approach the students positively and use a variety of teaching methods in accordance with the students’ level.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".