Virtual classrooms engagement among Jordanian EFL students during the pandemic of COVID-19 period
Bibliographic record
Abstract
Engagement is an essential aspect of the learning process.To attain academic success, students must always be engaged.Because of the epidemic's global spread, most learning has been moved online.Traditional students today face a new challenge with the proliferation of online education.This abrupt adjustment may impact their learning behaviour and willingness to embrace change.Consequently, their enthusiasm for studying may decrease dramatically.During COVID-19, this study investigated the level of engagement among Jordanian EFL students in their virtual classrooms.A "student course engagement questionnaire" was administered to 602 Jordanian EFL university students (males = 323, females = 297).The results showed that Jordanian EFL Students generally had a moderate engagement level during the epidemic.While skills, emotional, and interaction engagement factors received a moderate level of engagement, only the performance factor received a high engagement level.Fewer than one-quarter of Jordanian EFL students acknowledged dissatisfaction with their virtual English classes, whereas the highest percentage reported being somewhat satisfied.The study's sample perceptions towards their engagement level were statistically significant due to their gender and university level.The presence of a teacher adds substantially to the development of interactive engagement communities and, as a result, the improvement of students' language skills.When the teacher's role is creatively played, a constant teacherstudent interaction arises.These findings were taken into consideration when providing limitations and recommendations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".