Motivational Challenges in E-Learning at the University of Bisha, Saudi Arabia
Bibliographic record
Abstract
During the COVID-19 pandemic, schools, colleges, and universities switched from their traditional modes of classroom teaching to online/e-learning. This has boosted e-learning culture and made it a common practice in Saudi universities. Blending e-learning and traditional teaching methods in educational institutions empowers today’s students with online systems to support their pursuit of academic knowledge and skills. Because this is a recent endeavour in Saudi Arabia, it presents many challenges. There has been very little serious research done on the best practices to improve students’ e-learning outcomes. This study investigates the motivational and technological impediments encountered by Saudi students, using a questionnaire as a research tool to explore their real life e-learning experiences and issues at the University of Bisha, Saudi Arabia. The objective was to identify effective motivational strategies to engage learners. This in-depth study yielded many firsthand insights into issues that impede students’ motivations in e-learning. After analyzing each problem, the paper proposes some concrete, innovative tips and teaching strategies for both the teachers and the students to make a feasible and significant difference in the e-learning practice.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".