Exploring the role of digital citizenship and digital empowerment to enhance academic performance of business students
Bibliographic record
Abstract
In the changing educational environment brought about by the COVID-19 pandemic, this study investigates the dynamic interactions between digital citizenship, digital empowerment, and academic performance. Data is collected from a survey of students at the College of Business, Al-Ahliyya Amman University. The Partial Least Squares (PLS) method of structural equation Modeling (SEM) was used to analyze the collected data. The findings reveal how digital empowerment is directly impacted by digital education, respect and protection. It was also discovered that digital empowerment directly improves academic performance, highlighting the significance of promoting digital settings that are safe, respectful, and conducive to learning. With the changing educational landscape, this study adds to a better understanding of how digital citizenship influences students' academic performance and learning experiences. It provides educators, organizations, and legislators with useful information to help them better prepare students for success in a fast-evolving educational landscape by enhancing their digital skills and competences.
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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 | 0.006 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".