Role of Technology in Supporting English Language Learners in Today’s Classrooms
Bibliographic record
Abstract
This qualitative research study examined the role that technology plays in supporting Kindergarten to Grade 8 English Language Learners (ELLs) in the classroom. The purpose of this study was to identify different teachers’ methods and strategies used in the classroom to support ELLs, as well as to identify some technological tools, such as computers, tablets, and Smart Boards that can be used to assist classroom teachers and English as a Second Language (ESL) teachers and their students during the language learning process. The data collected from an in-depth literature review and two interviews with experienced teachers from different grade levels were analyzed. Five themes emerged from the findings and included: 1) A variety of teaching strategies support ELLs during the learning process, including the use of technology such as computers, tablets, and Smart Boards; 2) Some benefits in using technology with ELLs include a positive increase in their independence and language skills; 3) Students and teachers face some challenges when using technology in the classroom, including technical difficulties, student engagement and off-task behaviour, lack of teacher familiarity with the technology, and new technologies not being children/user-friendly; 4) Students, parents, and teachers have a positive perception about the use of technology in the classroom. The discussion explored some strategies teachers can use while teaching ELLs, the pros and cons of using technology in the classroom, as well as the way technology is perceived in the classroom by students, parents, and teachers. This paper is intended for teachers who are interested in using technology with their 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".