Strategies for Integrating Digital Technology in Classrooms to support English Language Learners’ Learning and Engagements
Bibliographic record
Abstract
Toronto is one of the most multicultural cities in Canada, and as a result, the population of English Language Learners (ELLs) only continues to grow in its classrooms. In a recent survey done by People for Education on Ontario’s Publicly Funded Schools, 73% of elementary schools reported they have ELLs who require language support (People for Education, 2015). Technology is the mechanism through which current generations of students are growing up and learning, and can be used as a tool for levelling the playing field for ELLs, allowing them to showcase their knowledge through a familiar medium. This research study focused on learning the different strategies teachers use to integrate digital technology to support ELLs’ learning and engagement in the classrooms. Data was collected through semi-structured interviews with two elementary teachers currently working in a publicly funded school board in Ontario. Participants used technology to differentiate instruction for ELLs in terms of the delivery of material, more time to process information and complete work at their own pace, and offering a different way to demonstrate understanding and learning. Teachers observed increased student engagement as digital technology provided students with opportunities to express themselves in different ways. Recommendations include creating a community network, e-platform, where teachers can mentor each other, share instructional practices, technological implementations, and questions and concerns.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".