Teaching LESLLA Learners How to Use Chromebooks: Challenges and Possibilities
Bibliographic record
Abstract
This paper is an exploration of how Low-Educated Second Language and Literacy Acquisition (LESLLA) learners can make the best use of Chromebooks in an English as a Second Language (ESL) classroom and move from emergent to building users of technology. First, I argue that many of the challenges that prevent instructors from using this technology happen at the commencement of the program, and, with proper preparation, these issues can be avoided. Next, I demonstrate how on-going instruction can be scaffolded for LESLLA learners. Finally, I maintain that motivation and empowerment are experienced by the learners which makes this instruction timely and personally-relevant. To conduct this 20-week project, I used Action Research and used a variety of data: work samples, lesson plans, researcher notes, and class discussions and evaluations. This paper outlines key considerations for classroom instructors or program leaders who plan on implementing technology programs for English as a Second Language (ESL) learners with low print literacy in any language.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".