The Adoption of Online Learning during the Pandemic: Issues, Challenges, and Future Directions
Bibliographic record
Abstract
The Covid19 Pandemic has shifted the entire momentum of the traditional education processes into an environment where students experience difficulties in the E-Learning program. This study determines the challenges encountered in the duration of the E-learning and its' effect on students' perceived learning and satisfaction during the Pandemic. The investigation study formulated an aggregate of 313 respondents on a snowball inspecting strategy. Frequency and simple percentage, weighted mean, Chi-Square Test of Independence, and One-way ANOVA were used to treat and interpret the data. The findings revealed that the students encountered difficulties through course quality, peer interactions, learning diversification, user-friendliness, and course design. Additionally, it was revealed that how they perceived these difficulties affects their perceived learning and satisfaction in the E-learning process. It was found out also that a higher level of challenges would associate with dissatisfaction and lower student perception in education. The study concluded that E-learning is a platform that should be present in the teaching and learning modalities in all institutions regardless of the situation. Additionally, the course quality, peer interactions, learning diversification, user-friendliness of the process, and its course contribute to increasing the student's perceived satisfaction in E-learning. Generally, the impact of the Covid 19 pandemic provides a manifestation that to improve student perceived learning and satisfaction; there is a need to intensify the execution in the administration, teachers, and the learning management systems used in a Higher Education Institution.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".