Promoting Strategic Writing by Postsecondary Students with Learning Disabilities: A Report of Three Case Studies
Bibliographic record
Abstract
To date, seven intervention studies have been completed evaluating the efficacy of the Strategic Content Learning (SCL) approach as a model for promoting self-regulated learning by postsecondary students with learning disabilities. Summaries of outcomes from those studies suggest that SCL participation can be associated with gains for students in task performance, metacognitive knowledge, motivational beliefs, and strategic processing across a range of academic tasks (i.e., reading, writing, math). Previous SCL research reports have focused on general outcomes across participants and across tasks, rather than describing the details of individual cases. This article redresses that omission by describing the process of SCL intervention and associated outcomes for three SCL participants working on a common task, namely, writing. By reporting three in-depth parallel case studies, this article clarifies how SCL instruction is implemented to promote strategic writing, illustrates how SCL instructional principles can be personalized in response to individuals' needs, and traces the relationship between SCL instructional activities and outcomes. The article closes with a discussion of implications for theory, research, and practice.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".