Implementation of Modern Distance Learning Platforms in the Educational Process of HEI and their Effectiveness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it