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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".