Language Learners’ Disengagement in e-Learning during COVID-19: Secondary Teachers’ Views
Bibliographic record
Abstract
The study looked at language teachers’ views of their students’ disengagement in e-learning during COVID-19. It described their efforts on how to engage the learners beyond the screen, where teachers have no control, and how to overcome the issues disturbing students’ engagement, motivation, and achievement. The study looked at learning engagement as an important requirement in e-learning, which is influenced by social factors including teachers’ communication with students, students’ interaction among themselves, and their collaboration in learning activities. This study used interviews, reports, and notes of 15 teachers to collect data from 15 language teachers in some Saudi secondary schools during the academic year 2020–2021. That year was completely delivered in e-learning platforms. The findings show that teachers ran into several difficulties to engage their learners in online sessions during that year; students lacked some ethics and requirements for e-learning; and technical issues disabled both teachers and learners from remaining in the learning engagement. These three main results propose a framework for educators, students, parents, and policymakers to deal with the obstacles and threats of learning engagement in online lessons. The study ends with suggestions for future studies.
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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 | 0.004 | 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 | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".