Digital Literacy and Moral Values in the Digital Environments: Secondary Students' Perceptions
Bibliographic record
Abstract
There is an inevitable trend that directs not only social but also educational practices into digital world, and this trend should be examined in detail to recognize its effects on students. Keeping in mind this situation, this research examines secondary school students’ perceptions of moral values in the digital environments (MVDE) and digital literacy (DL) through various variables. The research employs quantitative method and correlational survey design by reaching a total of 250 participants. The results show that participants’ perceptions of DL are mostly high while their perception of MVDE are mostly at intermediate level. Gender is not a source of significant difference in terms of either DL or MVDE. While grade is not a variable effecting DL, it has been found out that lower grades have statistically significant higher values in terms of MVDE. Those who use social media have significantly higher DL scores while the difference in MVDE is not significant in terms of the same variable. Online gaming, just like gender, is not a source of significant difference in terms of both DL and MVDE. Daily online time is not related to a significant difference in DL; however, those who are online more than three hours daily have significantly lower MVDE than those with less amount of online time. Finally, MVDE and DL are positively correlated at a moderate level and MVDE explains 19,6% of DL according to the results. To conclude, it should be encouraged for the citizens of a digital world to have higher MVDE as it would help them to be “better” in such a world, just like the moral values in real life that turns people into more desirable acquaintances.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".