Evaluating the User Experience of E-Learning in the Distance Education Program at Taibah University
Bibliographic record
Abstract
This study aims to assess the strengths and weaknesses of the Blackboard E-learning management system used in distance education programs at Taibah University in Madinah, Saudi Arabia. The study takes a descriptive approach, employing several survey tools to acquire data on various aspects of the e-learning experience from the point of view of student users, with the objective of informing university policy and decision making. Particular aspects of the e-learning experience considered include the role of e-learning in providing opportunities for learner interaction, the effectiveness of the various mechanisms for improving the e-learning experience, and overall attitudes toward e-learning. The results of the study show that there are statistically significant differences in the experiences and attitudes about e-learning among various demographic groups: namely, between students in different academic years, students in different academic departments, and between students that have received prior training in computers and those that have not. In light of the results, a number of recommendations are made. It is recommended that there be increased cooperation between the various sectors of higher education and pre-university education, to ensure the spread of the culture of distance education among learners before they join the university. Intercommunication about the e-learning experience between different universities is also recommended, as is the hiring by institutions of higher learning of distance education experts.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".