Utilization of Self-Learning Modules and Pupils’ Academic Performance during the Transition Period
Bibliographic record
Abstract
The utilization of self-learning modules is a great help for pupils due to their enhanced quality as a learning material that will support teachers in the classes. This research study investigated the extent of the utilization of SLMs in the transition period from MDL to face-to-face and the pupils academic performance in the First Quarter of S.Y.2022-2023. It was conducted among twelve (12) Public Elementary Schools of West II District in the DepEd Division of Cagayan de Oro City with a total of one hundred (100) respondents. The study used a descriptive correlational method, and the survey data is analyzed through mean, standard deviation and Pearson r correlation. The study showed that the SLMs in the transition period in terms of activating previously learned material were highly utilized. The pupils have Very Satisfactory academic performance for the First Quarter. In the utilization of SLMs, the variables engaging with new material, proving ones competence and application in the real world, have a significant relationship to the pupils academic performance. It is recommended that teachers need to consider the use of new material or the SLMs to improve the delivery of lessons and instruction, assessment of learning, support mechanism, and development of learning resources in creating a productive learning environment in the classroom. Also, pupils should always be encouraged to completely commit to learning, answering, and doing different tasks when using the SLMs to further improve their academic performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".