LEARNING GAP ASSESSMENT IN FILIPINO SA PILING LARANGAN (TECH-VOC)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aimed to determine the learning gaps in Filipino sa Piling Larangan (Tech-Voc) for the First Quarter of School Year 2022-2023. A quantitative quasi-experimental research using a pretest-posttest design was employed in this study. It was conducted to the 33 Grade 12 TVL students of St. Paul University Surigao during the First Quarter of School Year 2022-2023. A validated test was used in conducting the pre-test and post-test in assessing the learning gaps in Filipino sa Piling Larangan (Tech-Voc). Five competencies that were least mastered showed significant progress and improvement. However, despite the interventions implemented, the learning gap resulted in only average mastery. As to their performance in terms of scores for pre-test most of the students belong to the average. After the intervention given, most of the students belong to the good level during the post-test. Furthermore, it showed that there was a significant difference in the pre-test and post-test results after implementing an intervention. It is recommended that Paulinian Filipino teachers may reassess the areas that require additional attention in instructional delivery to address the persistent learning gap and achieve mastery, despite the interventions that were implemented.
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.
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 | 0.003 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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 it