Bridging the Gap in Learning: Differentiated Learning to Enhance the Students' Reading Comprehension of Explanatory Texts and Writing Skills
Bibliographic record
Abstract
Text-based language learning in senior high school aims to introduce students to different kinds of text: fiction and non-fiction. The teaching of an explanation text as a non-fiction text aims to make students understand and be able to write it. Teachers can use various techniques to achieve the goals, such as differentiated instruction during the teaching process. This research scrutinizes how differentiated instruction helps students enhance their understanding and writing skills in explanation texts, particularly for students of junior high school level. Employing a pre-experimental One-Group Pretest-Posttest design, the data in this research were collected using observations, surveys, and tests. The data analysis compared the pre-test and post-test results with some criteria and percentages. The result shows an improvement in the student's understanding of explanatory texts, with an average score of 60,67 to 87,00 and a percentage of 60%. This study is expected to shed light on the teacher's use of differentiated instruction in teaching various kinds of text in formal and informal contexts.
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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 | 0.000 | 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) | 0.002 | 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".