Differentiated Instruction in a Public Junior High School: EFL Teachers’ Perception and Practices
Bibliographic record
Abstract
This qualitative case study was conducted in one of public junior high school in South Sulawesi, Indonesia. The study was aimed to explore the English teachers’ practices in differentiated instruction (DI) and their perception toward the curriculum policy. The data were collected through open-ended questionnaire and interview. The participants were EFL teachers who have implemented DI. The data were analyzed using thematic analysis. The findings elucidate English teachers' implementation, revealing three overarching themes: planning, implementation, and evaluation. In the planning phase, identification, curriculum analysis, and preparation emerged as crucial aspects. The implementation stage highlighted variations in teaching materials, guidance provision, and variation in evaluation. Evaluation encompassed assessment processes, learning outcomes, and reflective practices. The second result showed the teachers’ misconception regarding the implementation of DI, namely, chaos class and concern about fairness. The teachers found the challenges in implementing DI, namely determine assessment, class management and need extra time to allocate the DI. However, the implementation of DI gave the advantages in learning English, such as students’ engagement, understanding in learning and building inclusive environment. For the better quality of DI, the teachers need several supports from the policy makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".