Scaffolding Self-Regulated Learning for English as an Additional Language Literacy Learners
Bibliographic record
Abstract
Emergent multilingual English as an additional language literacy learners (EALLs) have unique learning needs as they are learning to read and write for the first time in any language while they are also beginning to develop formal learning strategies that support successful school-based learning. Consequently, EALLs require specialized instruction in how to regulate the metacognitive, cognitive, behavioural, motivational, and emotional aspects of learning in formal classroom environments. Theories of self-regulated learning can inform English as an additional language (EAL) literacy programming and guide instructors in the development of EALLs’ formal learning strategies. The effective use of formal self-regulated learning strategies for planning, monitoring, and evaluating learning is essential for successful school-based learning. In this paper, we review three models of self-regulation (Dörnyei, 2005; Oxford, 2017; Zimmerman, 2013) that inform an instructional sequence designed to support EALLs’ self-regulated learning in the classroom. We describe our research-informed instructional sequence and provide examples of how instructors can encourage EALLs’ use of self-regulatory strategies including commitment, metacognitive, satiation, emotional, and environment control strategies, as well as the development of metastrategies that support self-regulated learning. In summary, we demonstrate how research on self-regulated learning can inform instructional practices for EALLs in EAL literacy classes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".