Printed Self-Learning Module Distribution and Completion Preferences of Grade 7-12 Students of Tagugpo National High School in Davao Oriental, Philippines: A Conjoint Analysis
Bibliographic record
Abstract
This study was conducted to determine the printed self-learning module distribution and completion preferences of the students of Tagugpo National High School, Tagugpo, Lupon, Davao Oriental, Philippines. It is descriptive survey research wherein one-hundred eighty-four randomly selected student respondents are shown various choices or hypothetical profiles and asked to evaluate these profiles based on their preferences. To determine the overall preference of these students for printed self-learning module distribution and completion, conjoint analysis was done. The analysis revealed that students from Tagugpo National High School expressed a preference for the modules to be printed in booklet form, distributed within their respective barangays on Mondays, and collected at the conclusion of each quarter. They also preferred to be given only four subjects per week with a t wo-hour duration each and accomplish only the activities found in the modules with no summative tests. This study recommends that students may be given three options on how they want their modules to be distributed and accomplished. It also suggests that further study on self-learning module preferences may be done on a wider scale, including assessment revision and a link between module preferences and student learning outcomes and dropout intentions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".