Using E-learning System in Jordanian Universities during the COVID-19 Pandemic: Benefits and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the Coronavirus Disease 2019 (COVID-19) pandemic and the national lockdowns implemented in countries around the world, many universities worldwide made the transition from face-to-face delivery to online learning using e-learning systems. However, the successful transition from traditional class-based learning to online learning depends greatly on understanding the challenges related to the implementation and use of e-learning systems, as well as the technical and management factors that need to be enhanced. This study aimed to investigate the challenges related to the use of e-learning systems in Jordanian universities and to explore the technical and management aspects that impacted the successful implementation and use of e-learning systems during COVID-19. To achieve the study objectives, a questionnaire was developed by the researcher and distributed online to lecturers working at Jordanian universities. A total of 184 lecturers participated in the study. Based on the findings, the study provides recommendations which will help higher education policy makers, university management teams, and software developers build strategies to ensure the successful implementation and use of e-learning systems during the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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 it