Evaluating The Usability And Accessibility Of LMS “Blackboard” At King Saud University
Bibliographic record
Abstract
King Saud University is in the process of adopting and implementing the interactive Blackboard Learning Management Systems (LMSs) with features that allow members of staff and teachers from different faculties to access, upload assignments, send quizzes, download content, and evaluate the academic progress of the members of faculty. However, many faculty members complain about the accessibility and usability of the e-learning software because of the perceptions that the interactive features are not user friendly. Little research has been done to evaluate the accessibility and usability of the e-learning software. The current study was conducted to answer the research questions on the accessibility and usability of the blackboard vista e-learning software and the barriers of user experience when interacting with blackboard. The study was based on the hypothesis that Blackboard LMS is highly accessible and usable by teachers in the King Saud University and a hypothesis that stated otherwise. The elements that were evaluated using questionnaires include the design user interface, navigational features, and ease of use to answer the research questions. The results proved the hypothesis that ‘Blackboard LMS is accessible and usable by the teachers from different faculties for the delivery of content in the King Saud University. However, the study recommends that university should customize the e-learning software to the needs of the teachers to offer courses in English and in Arabic to increase and enhance the accessibility and usability of the software.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".