Engaging LESLLA Learners During Covid-19: The Nexus of Reading Strategies and Digital Tools
Bibliographic record
Abstract
For LESLLA instruction to be successful, it should include creating an engaging learning environment that relates instruction to learners’ lives, designing separate learning stations for individualized projects and more independent learning, and choosing adult-appropriate materials. Pivoting on a dime to cope with Covid-19 has illustrated the need for incorporating training in language skills alongside digital tools not only for second language (L2) learners but most importantly for LESLLA learners. This paper describes the professional experiences of a LESLLA teacher attempting to identify the relationship between reading strategies and new technology-rich instructional practices to support LESLLA learners during the pandemic. It highlights a promising technology integration framework known as the Substitution, Augmentation, Modification, Redefinition (SAMR) model (Puentedura, 2006, 2013) in an attempt to find the nexus of reading strategies and digital technology tools in an online LESLLA class in Western Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".