Learning gap in Filipino sa Piling Larangan (Teknikal-Bokasyunal): An assessment
Bibliographic record
Abstract
This study aimed to identify the learning gaps in Filipino sa Piling Larangan (Teknikal-Bokasyunal) during the first quarter of the 2022-2023 academic year. A quasi-experimental study using quantitative method with a design involving assessments before and after were utilized. The research was carried out with 33 TVL 12th Grade learners in the first quarter of the school year 2022-2023 at St. Paul University Surigao. The researcher administered an authenticated assessment for the preliminary and concluding evaluation, finding the disparities in learning in Filipino sa Piling Larangan (Teknikal-Bokasyunal). Significant improvement was observed in five competencies that were initially least mastered. Nevertheless, even with the interventions applied, the resulting mastery level was only partial. Regarding preliminary evaluation performance, most students scored in the fair range. After the interventions, most students achieved a satisfactory level in the concluding evaluation. Additionally, a notable distinction existed on the preliminary and concluding evaluation scores following the intervention. It is suggested that Filipino teachers at St. Paul University Surigao review the instructional areas that need further enhancement to close the ongoing learning gaps and attain full mastery, notwithstanding the implemented interventions.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".