Multiliteracy-Based Differentiated Instruction for Language Learning in the Center of Excellence Vocational High Schools
Bibliographic record
Abstract
Multiliteracy-based differentiated instruction has gained importance as an approach to address diverse learning needs, particularly in vocational education settings where students benefit from tailored content that aligns with their learning styles and competencies. This study explores how multiliteracy-based differentiated instruction impacts students’ engagement and multiliteracy skills at Center of Excellence Vocational High Schools in the Special Region of Yogyakarta, Indonesia. A qualitative case study design was employed, with data collected through documentation, observations, and interviews. Data were analyzed using an interactive model that included data condensation, display, and verification. The findings reveal that introducing digital content and platforms, such as e-books, laptops, and multimedia resources, significantly enhances student engagement and multiliteracy when used alongside diagnostic assessments and personalized feedback mechanisms. However, issues related to device compatibility and network access present barriers to optimal learning experiences. The findings indicate that enhancing technology infrastructure and implementing continuous teacher training is crucial to fully actualize the advantages of multiliteracy-based differentiated instruction. This approach holds significant implications for vocational education, as it equips students more effectively to meet the demands of an increasingly digitized workforce. Future research should investigate the long-term impacts of these methods on student achievement and explore the role of institutional support in sustaining effective instructional practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".