Results of the Study on Digital Citizenship of Undergraduate Students at Suan Dusit University, Thailand
Bibliographic record
Abstract
This research aimed to study of the digital citizenship of undergraduate students at Suan Dusit University (SDU) and compare the digital citizenship of undergraduate students at SDU studying in different faculties. The research sample consisted of 400 undergraduate students of SDU, 2nd semester, academic year 2024. Data were collected using the digital citizenship of undergraduate students at SDU scale, 5-point rating scale, 50 items, and were analyzed using Mean, Standard Deviation, One-way ANOVA, and Scheffe’ test. The results of the analysis found that: the undergraduate students of SDU had a high level of digital citizenship, and undergraduate students of SDU who studied in different faculties had significantly different digital citizenship at the .05 statistical level. The students of the Faculty of Education had a digital citizenship significantly different from the students of the Faculty of Humanities and Social Sciences, the Faculty of Science and Technology, and the School of Culinary Arts at the .05 level, with the students of the Faculty of Education having a higher digital citizenship than the three faculties.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".