Effectiveness of Pandemic Activated School Strategies (Pass) on Submission Compliance Rate of Selected Grade 10 Learners
Bibliographic record
Abstract
This research examined the effectiveness of Pandemic Activated School Strategies (PASS) on the submission compliance rate of selected Grade 10 learners. The two strategies embedded in PASS are Power of 2 and Individualized Project Message 853 (TLE), which are both initiatives for and by TLE/TVL Department. The PASS intervention was implemented from the start of 3rd quarter to 4th quarter of SY2020-2021 to a designated experimental group. The research revealed that there was a significant increase in the submission compliance rate of learners from the experimental group when PASS intervention was implemented in 3rd and 4th quarter. From a low of 8.70% and 21.74% out of 46 learners in 1st and 2nd quarter respectively, noticeably there was a huge improvement in 3rd quarter with 84.78% submission compliance rate or 39 out of 46 learners were submitting complete activity outputs. Consequently, the performance rating of each learner also made progress as submission compliance rate improved. When PASS was implemented in 3rd quarter, the computed average grade increased to 88. The t-test p-value of 0.0154 in 3rd quarter and 0.00002 in 4th quarter indicated that the performance rating of the experimental group is statistically higher than that of the controlled group. The results acquired from the research indicated that Pandemic Activated School Strategies (PASS) was effective in improving the submission compliance rate of selected Grade 10 learners. Moreover, this shows that assessment and feedback with remediation are vital to the learning process in this time of pandemic.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".