ERIC ED479113: Unlocking Potential: Key Components of Programming for Students with Learning Disabilities.
Bibliographic record
Abstract
This guide provides information to assist in developing and monitoring programming for students with learning disabilities. It focuses on key components of programming based on research and best practices. Expected outcomes of implementing the suggested strategies are described for each key component. The guide stresses that these key components of programming are not discrete but must work together to be effective. A section is given to each of the nine programming components and includes an explanation, barriers to the components implementation, ways to facilitate the component, expected outcomes and results, and connections to other Alberta (Canada) learning resources. Key components are: (1) collaboration; (2) meaningful parent involvement; (3) identification and assessment; (4) ongoing assessment; (5) Individualized Program Plans; (6) transition planning; (7) self-advocacy; (8) accommodations; and (9) instruction. The following three sections apply the key components to early school years, upper elementary school years, and junior high/senior high school covering the domains of metacognition, information processing and communication, social development, and academic development. Nineteen appendices provide specific tools including worksheets, checklists, tips for parents, an observation guide, lists of accommodations, and teaching rubrics. (Contains approximately 90 references.) (DB)
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.060 | 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".