The Development and Implementation of Learning Material on Exposition Text to Improve Students’ Achievement on Bahasa Indonesia
Bibliographic record
Abstract
The development and implementation of learning material on exposition text with a process for Senior High School (SHS) students on the teaching of Bahasa Indonesia is reported. The study is aimed to develop a learning material on exposition text with a process to be used as a learning media on the teaching of writing in Bahasa Indonesia. The study is carried out at state senior high school (Sekolah Menengah Atas, SMA) Medan, Indonesia at academic year 2017/2018. The learning material is developed by enriching the topics with exposition text to meet the competence required by national curriculum in Indonesia. The learning material has been used as a media the teaching and learning activities in the experimental class and compared with ordinary book that has been used in control class on the teaching of Bahasa Indonesia. An excellent learning material has been obtained. Implementation of the learning material in the class has change students learning style moving from teacher centred to students centred learning. The developed material was found very effective as a learning media in the teaching and learning process and be able to improve students’ achievement in Bahasa Indonesia. Students’ achievements in experimental class (M=89.63±5.32) was higher than that obtained in control class (M=77.25±5.06). The students’ writing skills are improved. Students performance in experimental class (M=86.04±6.89) was higher than that in control class (M=78.60±5.42). The learning facilities provided in the learning material are found to be effective to guide the students to improve their learning ability to write texts on exposition text with a process in Bahasa Indonesia.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".