Potentials of the Multiliteracies Pedagogy for Teaching English Language Learners (ELLs): A Review of the Literature
Bibliographic record
Abstract
This article presents the results of a review of published literature on the use of the multiliteracies pedagogy to teach English Language Learners (ELLs). A total of 12 studies were selected for the literature review based on three inclusion criteria: (1) studies using the multiliteracies framework or other aspects of the multiliteracies pedagogy such as multimodality; (2) studies with ELL participants; and (3) studies conducted within the last 10 years. Through a detailed review and analysis of these studies, five emerging themes related to the potential benefits of the multiliteracies approach were identified and discussed in this article: (i) student agency and ownership of learning; (ii) language and literacy development; (iii) affirmation of students’ languages, cultures and identities; (iv) student engagement and collaboration; and (v) critical literacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".