ESL Teachers Experiences and Perceptions of Developing Multilingual Learners' Academic Literacies
Bibliographic record
Abstract
This dissertation explores the development of the Academic Literacies of multilingual learners in the multicultural settings of a Toronto College. More specifically, it addresses teacher reported experiences and reported perceptions of developing the Academic Literacies of multilingual learners. It uses a unified conceptual framework encompassing established aspects of ESL materials and methodology, namely, teacher roles, learner roles, materials’ roles, activities’ roles, and academic literacies development roles to establish essential differences between the plural-noun Academic Literacies and other forms of single-noun literacy approaches. It applies this theoretical and methodological frame to the analysis of its three kinds of data sets: One, an online email Survey; Two, oral face-to-face Interviews; and, Three, written Reflections. It uses an online Qualtrics survey to establish teacher perceptions of the development of multilingual learners’ Academic Literacies. Then, it uses face-to-face interviews to examine teacher reported experiences in developing multilingual learners’ Academic Literacies. Finally, post-interviews, it analyzes its participants’ reported perceptions of developing the Academic Literacies of multilingual learners. Its findings establish the presence of consistent teacher attempts at incorporating learner backgrounds, fluency levels, and individual goals in creating a learner-centered and inclusive literacy education in an academic atmosphere that honors students’ real-life aspirations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 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".