The Undesirable Behaviors of Students in Academic Classrooms, and the Discipline Strategies Used by Faculty Members to Control Such Behaviors from the Perspective of the College of Education Students in King Saud University
Bibliographic record
Abstract
This study aimed to identify the undesirable students’ behaviors in academic classrooms, and the disciplinary, preventive and therapeutic strategies that will be used by faculty members to control those behaviors from the perspective of the College of Education’s students in King Saud University. The results of the study has shown that the undesirable behavior in academic classrooms that strongly apply to the sample are: cheating and plagiarism regarding homework and research, replying with a rude manners, using cell phones, side talking, and arriving late to lectures. And in regards to the discipline strategies that are used by faculty members, which strongly apply to the sample, and are related to co-educational assets, are: submitting a detailed plan at the beginning of the semester, establishing clear and concise discipline rules in the classroom and strictly follow them, explaining the consequences of not following the classroom discipline rules, treating students with respect and without mockery or embarrassment, and maintaining eye contact. In addition, the therapeutic disciplinary strategies are: giving a first notice to the student to remind him or her of the discipline rules, asking the student calmly but strictly to stop the undesirable behavior. The study has come up with a number of recommendations and suggestions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".