Evaluation of Digital Competency of Public University Students for Web-Facilitated Learning: The Case of Saudi Arabia
Bibliographic record
Abstract
Our public universities in Saudi Arabia have made considerable investments in digital hardware, on-site training, and online tutorials to improve the quality of e-learning. However, there is an observed gap among students between the expected and actual use of digital technology in their learning. To close that gap, this requires a conceptual evaluation model that illustrates technological actions students are involved in, the level of digital proficiency they are in, type of digital technology they use, and kind of support they need. This study used the Digital Competency Profiler to evaluate the digital competency of public university students in Saudi Arabia. Data on 94 students from a public university were collected using an online platform. Multiple procedures were used for instrument validation, data screening, and data analysis. Findings from the study suggest that the majority of public university students had high digital readiness for performing social and informational skills through smartphones. In addition, most of university students missed all skills in the epistemological competency and some technical skills. Finally, implications for practice, limitations for generalization, and directions for future research are presented.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".