“COVID has Brought Us Closer”: A Proleptic Approach to Understanding ESL Teachers’ Practices in Supporting ELLs In and After the Pandemic
Bibliographic record
Abstract
This paper uses “prolepsis,” a process of reaching into the past to inform present and future practices, to understand 12 English-as-a-second language (ESL) teachers’ practices of supporting English language learners (ELLs) through remote teaching during the COVID-19 pandemic from 2020-2021 in British Columbia and to envision some different current and future post-pandemic classroom literacies for diverse learners. Accounts of these ESL teachers’ synthetical moments of teaching and supporting ELLs during the pandemic suggest that they had to navigate “new” areas of teaching, including attending to students’ social-emotional learning (SEL), connecting with ELL parents, teaching and engaging students via technology-supported instruction, and co-teaching with mainstream teachers, on the basis of limited or no pre-pandemic experience. These insights suggest a need to widen the focus on ESL teachers’ knowledge and expertise in applied linguistics and instructional strategies to include classroom literacies in integrating SEL into ESL instruction, adopting interactive, student-driven instructional designs and practices afforded by multimodal technologies, maintaining multiple channels of communication with parents and students, and team-teaching with classroom teachers to provide tailored language support for ELLs.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.045 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| 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".