Understanding Teachers’ Views, Experiences and Their Strategies for addressing Aggression among Preschool Children in Botswana: The Case of the South East District
Bibliographic record
Abstract
Even in the early years of childhood life, some anti-social behaviours may manifest as aggression. Although the nature of aggressive behaviours may be justified or not, accidental or not, their prevalence in preschools is a problematic issue. Research links early childhood aggressive tendencies to detrimental effects on the psychosocial well-being of a child, either at an early age or later in life. Therefore, preschool teachers are tasked with the critical role of overseeing the development of children’s social tools to regulate emotions during episodes that could lead to the manifestation of aggressive tendencies. Using a qualitative cross-sectional case study design, the researcher’s purposefully and deliberately explored six teachers’ perceptions of aggressive behaviour as well as pedagogical practices used to demotivate the manifestation and prevalence of aggressive tendencies. Data were collected from teachers, journals, and a focus group interview session in the South-East district of Botswana, specifically in Ramotswa, to accomplish this study. The social learning theory (Bandura, 1973) and Bronfenbrenner’s Ecological Systems Theory were used to understand teachers’ views, experiences, and strategies in addressing aggression to understand the problem studied. The findings illuminated that various forms of aggressive tendencies are rife in preschools with varying degrees of violence. This made teachers realise that there are thwart episodes of students’ aggressiveness through differentiated, individualistic, and educative classroom management practices. Based on these key findings, the study concludes that there is a need for collective input from parents, teachers, and relevant stakeholders in efforts to prevent the emergence of and combat aggressive tendencies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".