What Determines Student Satisfaction in an E-learning Environment? A Comprehensive Literature Review of Key Success Factors
Bibliographic record
Abstract
COVID-19 has significantly changed the teaching-learning process and it may indeed be a permanent change. Schools, colleges and universities have had to switch to remote/e-learning in an attempt to continue their operations during the pandemic. Institutions have struggled to identify the key success factors necessary for effective e-learning. While there have been some studies that have identified a few key factors, there has not been a comprehensive review of the key success factors for effective e-learning. This paper fills that gap by presenting a detailed examination of the critical success factors required for effective e-learning. The results show that success in e-learning is a complex combination of key factors such as institutional/administrative support, systems configuration and technical design, the level of computer skills among learners, learners’ interpersonal behavior, e-learning readiness, learner motivation, computer anxiety, self-efficacy, instructors’ characteristics, environmental factors and the demand it imposes on learners of varying age and cognitive maturity.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".