Utilization of Language Strategies in Teaching Grade 10 Mathematics of Narvacan National Central High School
Bibliographic record
Abstract
This study aimed to evaluate the utilization of Language Strategies in teaching Grade 10 Mathematics at Narvacan National Central High School during the 4th quarter of the SY 2022-2023. Specifically, it focused on the profile of the students, their level of performance in language strategies, and their mathematics performance. It further determined the relationship between the students’ profile and the level of performance in language strategies, the students’ profile and their level of participation, the students’ performance in language strategies and their mathematics performance, and the problem encountered by the teachers in teaching mathematics using language strategies. This study used a quantitative research approach employing the correlational research design. The research design is a combined description, evaluative and correlational design. This study made use of a questionnaire consisting of two parts, the performance of the students when using language strategies and the 4th quarter grades of the learners in Mathematics. Hence, it is recommended students will participate in every activity or discussion related to the different language strategies. The performance of the students may be improved by sustaining the utilization of Language Strategies in Teaching Mathematics. Teachers and school heads are encouraged to innovate programs or activities that address issues related to low comprehension and low retention of students.
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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 | 0.002 |
| 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 | 0.002 | 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".