Study of 10th Grade Writing Skills through Claim-Evidence-Reasoning Framework
Bibliographic record
Abstract
This research determined the writing skills performance of 10th Graders of Academia de San Isidro Labrador in Talamban, Cebu City, during the School Year 2024 –2025, in terms of the utilization of the Claim –Evidence –Reasoning (CER) Framework in improving writing skills as basis for a teaching –learning experience guide. Specifically, the study sought answers to the following: the pre-test performance of the learners before the utilization of the assigned intervention (CER Framework); the post-test performance of the learners after the utilization of the assigned intervention (CER Framework); the comparison and seeking for a significant difference of the pre-test and post-test performances of the learners before and after the utilization of the CER Framework; the lived experiences of the learners during the conduct and utilization of the CER Framework; and the formulation of the teaching-learning experience guide.A one-sample pretest-posttest quasi-experimental design was employed in this study. Pre-test and Post-test Activity Sheets were given to 10th graders. It revealed that the learners of Academia de San Isidro Labrador improved their writing skillsafter using the CER Framework in their writing classes. Their post-test performance went significantly higher after the use of the CER Framework in the completion of their writing tasks. It also revealed that the CER Framework has played a role in improving their writing skills due to its step by step process of completing writing tasks as supported by the experiences of the learners when utilizing it. With this, it is recommended that the crafted CER Framework Teaching-Learning Experience Guide be integrated in various levels of writing classes.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".