DISTANCE LEARNING TECHNOLOGIES OF POSTGRADUATE DENTAL EDUCATION SYSTEM
Bibliographic record
Abstract
The implementation of distance learning is carried out with the help of modern systems of distance education. They allow to teach and to assess the knowledge of interns and doctors quickly and easily, regardless of their location. The aim of the study. A comparative review of the most well-known distance learning platforms, wich are designed to organize the learning process and control learning with the help of Internet technology. System of distance education is a virtual classroom with the possibility to train interns and doctors from different regions of Ukraine at the same time. There are many educational platforms for distance learning nowadays, such as Moodle (Australia), iSpring Learn LMS (Russia), Collaborator (Ukraine), eTutorium LMS (Ukraine), Opigno (Belgium), Atutor (Canada). Moodle is a free platform that allows users to create individual courses. It supports more than 100 languages. iSpring Learn LMS is a simple and user-friendly system that is a paid alternative to Moodle. Collaborator is a platform that works effectively on all modern devices and browsers and is virtually independent of the software of the user's device. eTutorium LMS is a virtual distance learning system that allows to create an online course of any complexity quickly. Opigno is a modern free distance education system based on Drupal (a popular content management system). Atutor, like Moodle, is an open web-based e-learning system. Conclusion. Distance learning systems differ not only functionally, but also in the way they solve problems. The simplicity of use of the platform depends on the degree of its adaptation to the needs of the user and the ability to use all existing features and functions of the system.
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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 | 0.006 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.022 |
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".