Classroom Management Practices and Academic Performance in Multigrade Classes
Bibliographic record
Abstract
Multigrade classes hold significant importance in the field of education as they address several critical needs simultaneously. This study sought to determine the levels of classroom management practices and academic performance of learners in multigrade classes and its relationship. It employed a descriptive-correlational research design with documentary analysis. This study utilized a survey questionnaire from TS MPPE (2017) and conducted to Two Hundred Twenty-Four (224) learners in four (4) multigrade schools of Talisayan District, Division of Misamis Oriental. The learners’ average grade for the First and Second Quarter of the School Year 2023-2024 was also used. It employed the Mean and Standard Deviation and Pearson Product Moment Correlation Coefficient (r) to ascertain significant relationship between classroom management practices of multigrade classes and learners’ academic performance. Results showed an overall high classroom practice with facilities and resources as very high. Learners have Very Satisfactory average rating for their academic performance. A significant relationship exists between classroom management practices and learners’ academic performance, thus rejecting the null hypothesis. It concluded that the availability of the facilities and resources like technology play a fundamental role in enhancing the academic performance of the learners. Thus, multigrade teachers may sustain the best classroom management practices to maintain if not to reach the highest academic performance of learners while they are in the multigrade classes.
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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 | 0.000 | 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) | 0.002 | 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".