Most Essential Learning Competencies (MELC) - Based Modules Learners' Mastery and Performance in Mathematics VI
Bibliographic record
Abstract
<p>The main thrust of this research was to assess the teachers' perception on the Most Essential learning Competencies (MELC) based modules in relation to Learners performance in Mathematics VI. The respondents of the study were Grade VI teachers and learners in the Division of Bohol. Specifically, this study sought to determine the mastery level of Grade VI learners in mathematics competencies in first to third quarters. This study used descriptive-survey, documentary analysis and correlation research designs to obtain the information needed. The study covered 3,655 learners and 731 teachers in Grade VI. The data of the study were computed and presented on tables using the weighted mean, Pearson-Moment Coefficient of Correlation theresults were analyzed and interpreted. Based on the findings the content were relevant, quality were moderately high and usability were described as useful. And the learned competency from first to third quarter falls to mastery level. Content was significantly related to learners performance while, quality and usability had slight relation to learners performance. After a thorough examination of the findings and conclusion of the study, the researcher recommends. On paying more attention on the learning materials used in classroom, to encourage to innovative materials and techniques in teaching mathematics and the necessity of reviewing the aspects of MELC - based modules. Furthermore, the math experts, subject area supervisors and math writers and teachers must collaborate and focus to the less to least mastered competencies. From the given recommendations, the researcher offers a proposed improvement measure.</p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".