Self Regulated Development Learning Model Based on Local Culture to Improve Elementary School Students’ Explanatory Writing Skills
Bibliographic record
Abstract
This study aims to develop a learning model to improve elementary school students' explanatory writing skills. This development research used the Plomp model (preliminary research, prototyping phase, and assessment). Data collection techniques were carried out by interview, observation, and documentation study. The data collection tool employed was a written test sheet for students’ explanatory writing skills. After conducting a needs analysis, it was found that the explanatory writing material is new in elementary school. Thus, the students' explanatory writing skills in this study were very low. The teacher then needed a model focusing on the students' development in explanatory writing skills. This research, therefore, produced a learning model of self-regulated strategy development based on local culture to improve the explanatory writing skills of elementary school students. This model can motivate and increase students’ confidence in writing by choosing several strategies provided and measuring, analyzing, and activating early abilities, thereby increasing students’ content knowledge. The results also revealed an increase in students’ explanatory writing skills. Moreover, this special model pays attention to how to write explanatory in elementary school. Further, this model cannot only be used in elementary schools but can also be used at higher school levels. Therefore, future researchers are advised to conduct research by testing the effectiveness of learning models with similar themes at higher school levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".