Determination of Digital Citizenship Levels of University Students at Sakarya University Turkey
Bibliographic record
Abstract
When digital transactions such as official transactions, banking transactions, communication, education, production, shopping are carried out in digital environment, the concept of digital citizenship has emerged. Digital citizenship; it is the person who has the ability to use information technologies appropriately and correctly in areas such as official transactions, social communication, education, and production. As technology improves, problems with its use increases exponentially. So technological behavior or technological citizenship it is clear that the behavior, values, ethical rules and awareness should be created. In this study in order to detection this awareness; it is aimed to determine the digital citizenship levels of university students. For this purpose, 253 students studied in various departments of the Hendek Vocational High School of Technical Sciences in Sakarya university. The research was carried out with the relational scanning model from the quantitative research method. “Digital Citizenship Scale for Youth” was developed by Kus, Gunes, Basarmak and Yakar (2017) the researcher as a data collection tool with permission. The scale has 49 items, the total variance of the scale was determined that the scale had 8-factor structure and was found to be 49,70%. Related dimensions are communication, jus and responsibility, critical thinking, participation, security, digital skills, ethics and commerce. In the analysis of the data, non-parametric tests were used in addition to the percentage, frequency and standard deviation values. As a result; students were revealed to be aware of digital citizenship’s sub-dimension.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".