Saving Angels Program: An Intervention for Students at Risk of Dropping Out (A Community of Practice Action Research)
Bibliographic record
Abstract
The Department of Education and the Schools Division of Camarines Sur, in its support to the mission and vision of the department to keep students in school, Bagacay National High School came up with the Saving Angels program, a community of practice for excellent school’s project that addressed the Students-at-risk-of-Dropping out. The Saving Angels initiative looked into the dropout rate of the school, the level of effectiveness of the program, and its effect on the dropout rate. The research followed a descriptive-quantitative design where the level of effectiveness was taken from a survey questionnaire, whereas the dropout rate was computed at the end of each quarter. The interventions conducted in this study are Teacher’s Bank, Model Class, Hiking Society, Gulayan sa Bawat Bagacayenong Tahanan, and Ambassadors of Academic Instruction. These interventions targeted the generation of instructional materials, classroom modification and attendance monitoring, student home visitation, open vegetable garden, and teacher leaders and heroes in instruction. A learning action cell served as the backbone of the sharing. The said interventions were conducted from June 2018 to February 2019. The program resulted in a decrease of the school dropout rate from 10.43% (52 students) in 2016-2017, 3.2% (20 from 623) in 2017-2018, to only 2.8% (20 out of 717) of 2018-2019. The program continues and grows to sustain the development of the Bagacañeno learners.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".