Transforming ESL Teaching Modalities Using Technological Tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it