A Proposed Assessment Criterion for E-Learning Sites Evaluation: An Experts’ Opinion
Bibliographic record
Abstract
<p>An increasing growth in the number of e-learning sites at universities and other educational institutions led to necessary of develop and adopt a standards element to assess these sites to ensure efficiency, rival, and educational quality. Therefore, this study proposed an assessment criterion to evaluate the e-learning sites as a guide for decision-makers in order to purchase and development e-learning sites in which these assessment criterions are commensurate with the learning process. An experts’ opinion from universities professors who specialize in the field of teaching in different Jordan universities have been considered in order to develop a proposed assessment criterion, the result shows that the assessment criterion to evaluate the e-learning sites has twenty six criterion under five main categorizes namely: website design, and scientific knowledge content, technical elements, operational elements and finally with credibility of information sites. Given that the proposed assessment criterion to evaluate e-learning sites is guide for students, teachers, owners and developers about the benefits of e-learning sites.</p>
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| 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".