Samoprocjena digitalnih kompetencija hrvatskih studenata Filozofskog fakulteta u Splitu i kanadskih studenata sa Southern Alberta Institute of Technology
Bibliographic record
Abstract
Contemporary man lives in a digital society. To be literate in a digital society does not just mean to be able to read, write and count. To be literate it is important to have other competencies such as digital competence. Digital competence is considered by the European Commission as one of the eight key competencies. She implies a secure, critical and creative use of ICT for achieving goals related to work, employment, learning, leisure and inclusion and/or participation in society. Therefore, the aim of this study was to examine differences in selfassessment of their own digital competence levels through five areas between Croatian students from the Faculty of Humanities and Social Science in Split and Canadian students from the Southern Alberta Institute of Technology. Questionnaire for Croatian and Canadian students was made for the purpose of the research. The questionnaire was conducted on a sample of 89 students from the Faculty of Humanities and Social Sciences in Split and on a sample of 36 students from the Southern Alberta Institute of Technology. The research results show that Croatian students from the Faculty of Humanities and Social Science in Split self-assess their own level of digital competence for five areas better than Canadian students from the Southern Alberta Institute of Technology. The results also show that students of Faculty of Humanities and Social Science in Split self-assess their own level of digital competence for five areas better than students of two-pronged studies at the Faculty of Humanities and Social Science in Split. The reason for this may be the fact that only selfassessment was used in the research, not objective evaluation like a knowledge test.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".