Student-Teachers’ Experiences and Strategies of Managing Disruptive Behaviours in Tanzania Secondary Schools.
Bibliographic record
Abstract
This paper reports on student-teachers’ experiences during a six-week teaching practicum of disruptive classroom behaviours by students in selected Tanzanian secondary schools and the strategies that the student-teachers employed to manage them. Questionnaire and semi-structured interviews were employed to collect data from 70 student-teachers. Using qualitative thematic analysis and descriptive quantitative analysis strategies, it was revealed that student-teachers did very little to enhance appropriate classroom behaviours. Instead, they relied on punitive strategies such as punishment to deal with disrupting students. Reliance on punitive measures limited their ability to use positive feedback, tolerance and relational support strategies, which are regarded as more effective in fostering appropriate classroom behaviours by empowering students to take control of their own behaviour. These findings have important implications for teacher training programmes, and students learning. The paper concludes by asserting that like any other lessons, appropriate behaviours in classrooms need to be taught and nurtured not simply demanded.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".