COMPARATIVE ANALYSES OF E-LEARNING USING AMONG STUDENTS OF VOCATIONAL EDUCATIONAL IN GERMANY VERSUS RUSSIA
Bibliographic record
Abstract
The UNESCO experts as well as governments of many western countries confirm that e-learning as an educational technology has become a priority trend in educational reforms in the USA, UK, Canada, Germany, France and many other countries nowadays. Likeminded the European Commission's Lifelong Learning Programme 2007-2013 has been described as the new EU umbrella for education and training programs. In recent years the Russian system of the e-learning has developed as well and usually faced different challenges. Indeed Russia defined e-learning on the state level already in 1995. Later on legal issues were defined in federal acts (laws) such as About education, About Higher and Post-Graduate Vocational Education and in the order of the Russian Ministry of Education No4452 About adoption of the methodology of e-learning technologies in higher, secondary and vocational educational institutions in Russian Federation. Likewise in the Russian State Vocational Pedagogical University (RSVPU, Ekaterinburg) e-learning was started 2004. Since that moment the number of students enrolled in e-learning has increased almost 20 times. To allow a more valid international comparison this research has been conducted as part of the international Erasmus Mundus Consortium Multidisciplinary capacity-building for an improved economic, political and university co-operation between the European Union and the Russian Federation. In the sense of a comparative analysis students of vocational educational in Russia and in Germany had been surveyed. Specific basis has been the learning management system OPAL, the Saxon adaption of the Swiss based Open Source System OLAT. Overall comprehensive experience in Saxony in creating and implementing e-learning courses has led to one of most intensely used installations throughout Germany with more than 90.000 students. In order to identify readiness of students for e-learning, as well as comparing their different perceptions about advantages and disadvantages of e-learning authors surveyed groups of vocational education students enrolled at the TU Dresden (Dresden, Germany) and RSVPU (Ekaterinburg, Russia) using the platform OPAL, altogether 75 students (47 from Germany and 28 from Russia).
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".