Bridging Literacy Acquisition and Self-regulated Learning: Using a SRL Framework to Support Struggling Readers
Bibliographic record
Abstract
Reading is conceptualized as a hierarchy of component skills where lower order emergent literacy skills set the foundation for higher order reading skills such as fluency and comprehension. Approximately 20% of readers struggle within this hierarchical process (Fielding, Kerr, & Rosier, 2007). Struggling readers are susceptible to the Matthew Effect, a reading trajectory that sees them fall further behind their grade-level reading peers as they progress through their school grades. The purpose of this paper is to present a model of reading that considers self-regulated learning as an important motivational factor, particularly for children at-risk for reading difficulties. We describe reading acquisition within the context of task understanding and perceived self-efficacy, goal setting, strategies and tactics, and monitoring and feedback—the self-regulated learning process (see Winne & Hadwin, 1998).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".