Integrating Task-Based and Differentiated Instruction (DI) to Overcome Language Learning Challenges
Bibliographic record
Abstract
This research aims to overcome language learning difficulties faced by students with special needs by applying a task-based approach and differentiated learning (DI). Qualitative methods are used in analyzing data. The research took place at a special school at SLB Negeri 2 Denpasar. SLB Negeri 2 Denpasar is a special school for children with hearing impairments. In this remarkable school, students would learn to communicate by reading the movements of their lips. Primary data in this research refers to the exact information obtained from student grades and academic tests. The treatment is modified according to the children's cognitive capacities. The results showed that task-based and differentiated learning (DI) are effective techniques in supporting students with special needs. High school students with special needs require a different type of parental guidance. Students with special needs can receive learning support by limiting choices and guiding choices. Providing autonomy for students to engage in task-based learning can facilitate higher student learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".