VOCABULARY LEARNING STRATEGIES AS PREDICTORS OF ACADEMIC PERFORMANCE AMONG SECONDARY SCHOOL STUDENTS
Bibliographic record
Abstract
This study examined the relationship between vocabulary learning strategies and academic performance in secondary school students. A survey was conducted to a sample of 267 Grade 10 students from five public secondary schools in the District of New Corella, Division of Davao del Norte. It employed stratified random sampling to select respondents from the chosen schools so that each has an appropriate percentage of distribution of student respondents based on ratio and proportion. Adapted questionnaires were used to determine the level of vocabulary learning strategies and the researcher utilized the third quarter grades of the five schools in the district. The ethnicity shows that the total respondents are overwhelmingly Bisaya. The respondents are in the lowest income category, which suggests a clear bias toward constrained financials. Among the five indicators, determination strategies turned out to be the most often used, and social strategies were the least effective. However, the English grades of the students demonstrate a clustering of their scores around the average. There is no strong evidence to support the claim that there are differences in average English grades among the ethnic groups being compared, while there is no significant difference in the mean English grades among the four income categories. It is recommended that students should actively use effective vocabulary learning strategies to enhance their academic performance across subjects and that teachers design customized vocabulary acquisition. Thus, the administrators and school principals may monitor the implementation of vocabulary learning strategies to improve academic performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".