Transforming ESL Teaching Modalities Using Technological Tools
Bibliographic record
Abstract
A qualitative approach was used to analyze the effect of technology on enabling ESL students to grasp new content. The major objective of this research was to explore the techniques and strategies implemented by ESL tutors. The research also identified the technological tools such as smart board computers, and tablets that ESL teachers can use in passing information so as to allow students relate with whatever is being taught. Data was collected through conducting interviews on two ESL tutors who are highly experienced, and by conducting an in-depth literature review. In the findings, four themes became evident. They are; 1) Numerous techniques are applicable in teaching ESL such as tablets, computers, and smartboards; 2) A major benefit of incorporating technology in ESL is higher independency rates among students 3) Various challenges are normally faced by the tutors when using technology to teach ESL including lack of knowledge on how to use the provided technology, poor student engagement, failure of emerging technologies in being user friendly, and off-task behavior. 4) Teachers, parents, and students appreciate the use of technology in teaching and learning. Moreover, this research reviewed the specific strategies that are applied by teachers so as to ensure that there is better receptivity amongst students. This research paper is intended to help provide a better understanding to tutors who may want to incorporate technology in teaching ESL.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".