Implementation of Modern Distance Learning Platforms in the Educational Process of HEI and their Effectiveness
Bibliographic record
Abstract
The growing role of distance learning platforms at higher educational institutions in developing countries, and the inadequate study of their effectiveness have necessitated the elimination of this imbalance. An additional problem in studying the issue of platforms’ effectiveness is the limitation of studies, which is based on qualitative methods of assessing the effectiveness. The quantitative assessment of the effectiveness and level of satisfaction with the implementation of distance learning platforms at higher educational institutions has been conducted in this academic paper. The assessment has been conducted using the System Usability Scale (SUS) to assess the Usability of the Moodle remote platform in Ukraine and the User Satisfaction Questionnaire (USQ) to assess students’ satisfaction. The article proves the connection between the Usability of the distance learning platform and the level of satisfaction with its use. This provides an opportunity to improve the problem areas of the Usability platform in order to increase the efficiency of its use. The following effects of application have been revealed, namely: increase of internal motivation, involvement in the learning process, level of satisfaction from courses and training programs (curricula), recognition of homework’s importance, increased interest in subjects, and students’ self-efficacy. Effective communication and quick response, an automatic control system are factors that contribute to the introduction of modern distance learning platforms in the educational process of HEI. The important elements of the effectiveness of distance learning platforms in the educational process of HEI are interactivity, simplicity, convenience, the speed of student-teacher interaction, platform flexibility, and quality control.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".