Toward Digital Citizenship: Examining Factors Affecting Participation and Involvement in the Internet Society among Higher Education Students
Bibliographic record
Abstract
The current study aims to understand digital citizenship, based on the assumptions of Ribble (2014), by examining factors affecting participation and involvement in the Internet virtual societies among higher education students. A quantitative approach using a survey questionnaire was implemented. The participants were 174 students from the Faculty of Education at King Abdulaziz University in Saudi Arabia. The descriptive statistics show that the students generally have good levels of perceived Internet attitude, computer self-efficacy, and digital citizenship, especially in terms of respecting oneself and others online. The factors affecting digital citizenship are computer experience, daily average technology use, students’ attitudes toward the Internet, and computer self-efficacy. Students with higher levels of computer experience are more involved in activities related to educating oneself and connecting with others online compared with students with less experience. Further, students with higher levels of daily average technology use tend to protect themselves and others online more compared with students with lower levels of technology use. Moreover, higher levels of students’ Internet attitude and computer self-efficacy are associated with higher levels of respect for oneself and others, of educating oneself and others, and of total digital citizenship. Based on the current study findings, appropriate recommendations are proposed in terms of policy and practice.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".