Strategic evaluation of e-learning: A case study of the university of Jordan during crisis
Bibliographic record
Abstract
Crises possess a remarkable propensity to serve as catalysts for change, particularly within the sphere of education. They catalyze change in several key dimensions, including Accelerated Adoption of Technology, Highlighting Inequalities, Global Collaboration, Adaptation of Assessment Methods, Engagement with Online Resources, and Reimagining Education. In essence, crises compelling educational institutions to reevaluate long-standing norms and embrace innovative solutions. They shine a spotlight on challenges that may have remained overlooked and stimulate the formulation of strategies aimed at cultivating a more robust, adaptable, and inclusive educational landscape. This study provides a comprehensive strengths, weaknesses, opportunities, and threats (SWOT) analysis of E-learning at The University of Jordan amid the COVID-19 pandemic, offering valuable insights for strategic planning during crises. Based on data collected from 379 undergraduate students in the year 2022, it reveals strengths in terms of convenience and flexibility but highlights weaknesses such as low bandwidth and unstable internet connections. Additionally, it recognizes the opportunity for E-learning effectiveness during lockdowns while identifying threats like unreliable power supply and inconsistent internet access. This analysis enriches our understanding of the educational landscape and equips decision-makers with essential insights to enhance E-learning resilience and effectiveness during challenging times.
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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 | 0.002 | 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".