Educational triumphs in the face of crisis: The university of Jordan's e-learning satisfaction: Evidence from the pandemic time
Bibliographic record
Abstract
The onset of the COVID-19 pandemic has wrought a profound transformation in the higher education landscape, compelling universities worldwide to pivot towards E-learning as the primary mode of instruction. This study, which was performed on a sample of 330 undergraduate students at The University of Jordan, undertook an in-depth exploration of student satisfaction with E-learning at The University of Jordan during this unprecedented shift, examining it through four demographic variables: gender, residence, faculty type, and academic year. The researchers employed rigorous statistical analyses, including calculations of mean and standard deviation, as well as one-way ANOVA tests, to thoroughly examine the data. The findings indicate a strong overall satisfaction level among students regarding E-learning, with discernible variations between items. There is a noteworthy correlation observed between students' academic faculty and their academic year in relation to their satisfaction level, with a p-value below 0.05, indicating statistical significance. This underscores the nuanced influence of these demographic factors in shaping students' perceptions and contentment levels as they navigate the evolving landscape of higher education during this extraordinary period. While the gender and the residence have no effect on the satisfaction level.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".