INFORMATION AND COMMUNICATION TECHNOLOGY IN TERTIARY EDUCATION INSTITUTIONS IN SLOVENIA - A PREREQUISITE FOR E-LEARNING
Bibliographic record
Abstract
In 2003 research on information and communication technology presence and usage was conducted among tertiary education institutions in Slovenia. The basic purpose of the research was to draw up indicators for EU comparisons, following the requirements of Eurostat. Out of 84 Slovenian tertiary education institutions, 81 responded to the mail survey with telephone follow-up. The basic benchmark finding is that overall there are 5 PCs per 100 students. Better computer facilities are found in technical and natural sciences institutions, and private institutions in the fields of economics and education. The research results also show that employees are much better equipped, as there are more employee-used PC’s than employees. All institutions have Internet access, almost 60% of them also have their own e-mail servers, but only half of them provide e-mail addresses for their students on the domain of their institution. Exam registration and on-line exam results are provided by slightly more than a half of institutions, e-mail information alert about exam results is provided by one quarter of institutions, online application is provided only by one tenth of institutions. Distance online learning for certain courses/subjects is provided by 17% of institutions, whereas 9% offer distance learning for certain study programs. The majority of institutions are preparing their online degree programs - only 22% of them have no plans to offer e-learning (distance education) courses in the near future. E-learning education is most frequently provided by private institutions in the field of economics, and rarely found in the fields of medicine and health care, social sciences and education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".