Technology and English language learners: Can digital technology enhance the English language learning experience?
Bibliographic record
Abstract
This article focuses on the topic of technology and its role in elementary classrooms. Specifically, technology's use in supporting English Language Learners is researched, yielding results that are important for those working in the field of education, including pre-service teachers. Questions that are acknowledged in this article include: How can ELL students’ learning be enhanced with the use of technology? What technologies are best for supporting language learning needs? What advantages and challenges may arise with the use of technology? Both online survey and interview methods are used to collect data which are analyzed and collated using Google Spreadsheets and the word cloud software, Tagxedo. Results from research show that technology can be very beneficial in supporting ELL students upon a number of conditions being met: 1. The technology is intuitive and user friendly. 2. The technology/program does not replace the role of the teacher. 3. Teachers feel comfortable using the technology themselves to best support the learner. This may mean more training for teachers in the field of technology is necessary. 4. The technology enhances the child’s learning, rather than replaces a traditional practice that is just as effective. This research highlights professional roles and responsibilities, an important aspect of teaching, and is significant for educators striving to improve their teaching practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".