Developing the Digital Culture among the Students of Educational Faculties in Prince Sattam Bin Abdulaziz University
Bibliographic record
Abstract
The study was aimed to show the reality of practicing digital culture among students of the Education Faculties at Prince Sattam bin Abdulaziz University from their point of view, and to explain the obstacles and problems facing its implementation, and to monitor the university’s efforts in developing digital culture among its students. To achieve these aims, a descriptive method was used, and the researchers designed a questionnaire consisting of (32) items. It was distributed on three domains, and the sample was chosen randomly from the students of Prince Sattam bin Abdulaziz University (180), and the results revealed that the approval of the students of Prince Sattam bin Abdulaziz University on the reality of the university’s development of digital culture came with a medium degree. As for the university’s efforts to develop a digital culture among students came with a high degree, and the results showed that there are statistically significant differences in the viewpoint of the sample members towards the reality of digital culture among the students of the education faculties at the university according to the gender variable in favor of females, and specialization (in favor of kindergartens), and according to the academic year variable (in favor of the fourth year), the study recommends setting a strategic plan to promote digital culture and digital transformation in the education faculties and to invest modern communication technologies in supporting and developing educational technologies at the university.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".