Korišćenje elektronske knjige u vreme zdravstvene krize od strane studenata učiteljskih i pedagoških fakulteta Zapadnog Balkana
Bibliographic record
Abstract
The closure of educational and cultural institutions during the COVID-19 pandemic has led to changes in the traditional teaching model and the transition to online teaching. Since access to textbooks and professional literature was significantly difficult in completely new conditions, the resources of information and communication technologies were included in the teaching process, among which the e-book took an important place. The authors examine the experiences of students of teacher training faculties and faculties of education (N = 394) in relation to (a) the use of e-book for study purposes; (b) the impact of the COVID-19 pandemic on the use of e-book for study purposes and (c) the factors that initiated the use of e-book. Research results show that two thirds of the respond- ents use this book format. According to the experience of more than a third of students, the current health crisis has contributed to the use of e-books to a greater extent than before, while a quarter of the respondents were motivated to start using it. The use of e-books was initiated mostly under the influence of professors and by independent internet search. Future class teachers and preschool teachers from the Western Balkans are open to the use of modern technologies, and the current pandemic has intensified the use of e-books for the purpose of their education, which has contributed to overcoming the dif- ficulties caused by the health crisis, as well as to gaining competence in efficient work with future generations of readers of various forms of text.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".