Least Learned Competencies in Mathematics 8: Basis in Crafting Strategic Intervention Materials
Bibliographic record
Abstract
This descriptive research study was conducted to determine and analyze the least learned competencies in Mathematics 8 from First Quarter to Fourth Quarter in Naguilian District, Division of La Union as basis for crafting K to 12 aligned Strategic Intervention Materials (SIMs). It also looked into the problems encountered in teaching Mathematics 8. Grade 9 learners and teachers were the respondents; the learners’ participants were selected through Slovin’s formula. In treating the gathered data mean, percentages, frequencies, and ranking were used. It found that there were eight (8) least learned competencies in all the quarters (first, second, third, and fourth) and the level of performance was fairly satisfactory. It was concluded that the least learned competencies were those that require higher order thinking skills. It was recommended that the crafted K to 12 aligned Strategic Intervention Material should be adopted by high school teachers in Naguilian District and other high school teachers in the province as an additional learning material to address the weaknesses of the learners along the identified least learned competencies in Mathematics 8.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".