A Question of Calibration: A Review of the Self-Efficacy Beliefs of Students with Learning Disabilities
Bibliographic record
Abstract
This article reviews the literature on the self-efficacy beliefs of students with learning disabilities (LD). Motivational and metacognitive difficulties of students with LD are briefly discussed, followed by a synopsis of Bandura's self-efficacy theory, with special attention to the issue of calibration. From the literature search, 22 studies met the criteria of (a) using a measure of self-efficacy, and (b) including a sample of students identified as having learning disabilities. The resulting body of literature is summarized and analyzed in terms of the nature of the sample, the performance task or domain, the self-efficacy measure used, the research question and outcomes, and the accuracy of calibration between perceived self-efficacy and task outcome. The results from this review suggest that in specific contexts — in the writing performance of students with specific writing difficulties, in particular — students appear to optimistically miscalibrate their self-efficacy. Methodological problems found in some of the research, such as “conceptual blurring,” are discussed. Finally, implications for practice are considered, and suggestions are made for future research.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".