Classroom Management Practices and Learners’ Academic Performance
Bibliographic record
Abstract
A culture of mutual respect and trust in the classroom encourages learning and reduces disturbances. This study sought to identify the classroom management practices of teachers in terms of Rules and Procedure, Disciplinary Interventions, and Teacher-Student Relationships to determine the learners academic performance for the second quarter of SY 2022-2023 and find the significant relationship between classroom management practices and learners academic performance. The study utilized a quantitative descriptive research design. A survey was used to acquire quantitative data on teachers classroom management practices. It also used frequency, percentage, mean, and standard deviation. Pearson Product Moment Correlation was used to determine the significant relationship between the variables. The respondents of the study were the 302 students of North 3 District, Division of Gingoog City. Results showed that teachers Highly Practiced Rules and Procedures. However, not all the respondents agree that teachers are practicing or imposing Disciplinary Intervention. It can be concluded that teachers were able to establish clear set of rules and procedures that students can base their actions from which ensure better engagement and learning inside the classroom. It can be recommended that teachers need to improve the practice of disciplinary intervention in their classroom to better understand what is expected of them in terms of conduct and behavior.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 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".