Using Local-Wisdom Literature in Teaching English through Text-Based Method on Merdeka Belajar Curriculum
Bibliographic record
Abstract
Literary text-based learning is interesting because literature learning in schools is often neglected. Literature learning often focuses on knowledge-based learning. The changing curriculum does not guarantee a change in the learning system in elementary school classrooms. Therefore, this study aims to describe the process of teaching and learning text-based literature at the elementary school level and its impact on students’ writing skills. Qualitative and quantitative descriptive methods were used. A qualitative method was used to describe the teaching and learning process in the classroom, while a quantitative method was used to assess learning outcomes. Techniques used were observation, interviews, and narrative writing tests, with the research object being three school teachers and 60 students from 3 schools in Samosir Regency. The results showed that the Seventh-Grade secondary school teachers in Samosir Regency do not yet understand text-based literature learning techniques fully, so teachers still use conventional methods. The results of teaching and learning activities on students’ ability to write narratives showed an average score of 73.27. This value has reached the Minimum Completeness Criteria (KKM) but has not yet been maximized because there are still obstacles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".