Enhancement of Learners’ Attentiveness in ESL Classroom with CGW Using Web 2.0 Tools: An Intervention Study
Bibliographic record
Abstract
Students’ attentiveness in the classroom especially in ESL classrooms is an inevitable alignment of effective learning as well as teaching English as a second language. However, students are erratic in their attendance, and even when they do show up, they lack their attentiveness and are not focused on the lessons taught in the classroom. As a result, their ability to learn is being hindered more than ever. This is a significant issue in teaching and learning in tertiary-level education at most colleges and universities around the world. The research aims at reducing the lack of learners’ attention to learning in ESL classrooms and enhancing students’ attentiveness through the intervention of collaborative group work (CGW). The study was conducted in a mixed method that included a questionnaire survey, observation and some previous relevant Content Analysis (CA). The settings of the research were the departments of English of a College of the National University in Bangladesh and three colleges of the King Khalid University in Saudi Arabia. The study found that before an intervention, 62.75% of students were not paying attention, while only 31.75% were paying attention in their ESL classroom. But after the intervention, 63.50% of ESL students were more attentive, while 32.50% were no longer attentive. This indicated a significant change. So, the study successfully proved that if the collaborative group work (CGW) using web 2.0 tools were well performed among the students in the ESL classroom, the present and future learners could be capable of enhancing their attentiveness in the classroom and flexibly learning English as a Second Language than before.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".