The Extent to Which e-Learning is Being Utilized in Teaching the Islamic Education Curriculum in Ma'an Governorate Schools as Viewed by the Teachers
Bibliographic record
Abstract
This study aimed at revealing the extent to which e-learning is used in teaching the Islamic Education curriculum in the schools of Ma'an Governorate from the teachers' point of view. In order to achieve this, the researchers developed a questionnaire consisting of (20) items. The validity and reliability of the questionnaire were verified, which reached (0.86%). The study sample consisted of (70) male and female teachers. The descriptive survey method was used in order to achieve the objectives of the study. To answer the questions of the study, the arithmetic averages, standard deviations, one-way ANOVA and Scheffe test were calculated. The results showed that the most important teachers’ estimates of the extent to which e-learning is used in teaching the Islamic education curriculum in schools of Ma’an Governorate were as follows in descending order: the reasons for poor usage of e-learning, the availability of e-learning, the ability to choose appropriate e-learning methods, and the reality of using e-learning. Moreover, the results showed that there were no statistically significant differences attributed to the variables of gender and educational qualification. The results also showed the presence of statistically significant differences attributed to the variable of work experience in favor of the group of more than (14) years, in the field of reasons for poor usage of e-learning. The study recommended the necessity of conducting training workshops for new teachers, and providing the necessary infrastructure in the field of e-learning in all schools.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".