Managing Learners’ Behaviours in Classroom through Negative Reinforcement Approaches
Bibliographic record
Abstract
The present study aims to identify the types and levels of disruptive behaviours among students in classroom and the levels of negative reinforcement approaches practiced by teachers in managing and tackling these disruptive behaviours. A total of 119 teachers from four national secondary schools in Zone A, Miri, Sarawak were selected. Questionnaire was used to collect data and the data was analysed using descriptive and inferential statistics (one-way ANOVA). The research findings indicated that; (a) absenteeism especially entering classes late, and (b) defiance in classroom in which the learners refuse to join any social game activities conducted in classroom were among the highest disruptive behaviours displayed by the learners in classroom at the national secondary schools. The result of the present study also indicated the practice of negative reinforcement approach in the form of warning was higher than other forms of approaches (scolding and punishment). There was a significant difference with regard to the practice of negative reinforcement approach based on the teachers’ years of teaching experience. Implications of the current study towards teaching practice and educational policy are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".