Teacher Trainees’ Strategies for Managing the Behaviours of Students with Special Needs
Bibliographic record
Abstract
This study aimed to determine how a group of teacher trainees handled challenging behaviour by students during teaching practice. A total of 35 teacher trainees from the special education programme of a local university were chosen as respondents. A questionnaire based on a 5-point Likert-type scale was administered in this study. The data were analysed descriptively involving frequencies and percentages, mean scores and standard deviations. The results showed that the most frequent types of challenging behaviours shown by the students were joking and chatting with friends, as well as making noise, while the teacher was teaching. The most frequent action taken by the respondents to prevent the negative behaviours was to immediately reprimand the students and advise them. The respondents reported that the challenges they faced in managing behaviours in the classroom included limited time to manage behaviours, lack of skills and knowledge regarding behaviour management and heavy teaching workloads. The findings of this study imply that future teachers and teacher trainees should be exposed to knowledge and skills regarding behaviour management of students with special needs in real settings before they begin their teaching practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".