Ukrainian E-Learning Platforms for Schools: Evaluation of Their Functionality
Bibliographic record
Abstract
This article defines 27 criteria for evaluating the functionality of e-learning platforms, grouped into three macro groups: (a) learning management, (b) learning content management, and (c) communications and collaboration tools. The proposed criteria can be used to evaluate any e-learning platform’s functionality. They allow teachers and administrators to make conscious choices about the highest-quality e-learning platform for their schools and developers to improve e-learning platforms’ functionality. The developed criteria became the basis for rating the functionality of Ukrainian developers’ eight e-learning platforms' and determining the degree of support (in whole or partly) of e-learning components, categorized on the cognitive, social constructivist, motivation, and e-learning theories (CT, SCT, MT, and E-LT). The results indicate that the lack of communication and collaboration tools necessary to ensure quality distance learning is the main problem of Ukrainian e-leaning platforms. Comparative analysis of the functionality of e-learning platforms and components categorized on the learning theories helped determine that only three of the eight Ukrainian e-learning platforms (Accent [Mobischool], Class Assessment, My Class) fully follow the CT, SCT, and MT, but these platforms are all commercial products; therefore, they only partially support the E-LT. Solving this problem will be facilitated by developing e-learning platforms with open access, financed by the state budget in the context of the development of open and distance learning for Ukrainian students, as well as improving communication and collaboration tools in the context of conforming e-learning components to the social constructivist learning theory.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".