Internet platforms in the open educational environment in a foreign language for organising independent student work
Bibliographic record
Abstract
Relevance. The relevance of investigating web resources for learning in a globalised world in an open educational environment is due to the need to understand modern technical mechanisms involved in the educational process, their achievements and impact on learning. Purpose. The purpose of this study was to form an idea about the educational platforms of Kazakhstan and the world in general, examine the impact of the platforms under consideration on the educational process during the student�s independent work. Methodology. The following methods were used in a complex to solve the tasks and to implement the purpose of the study: analytical-synthetic, comparative, graphical, and statistical. Results. In this study, the following educational resources were compared: Duolingo, Samsung Global Goals, Google Classroom, AnkiDroid Flashcards, Coursera, TED-Ed, EdApp. They were considered in terms of the following parameters: interface and technical capabilities, availability of scientific materials, functionality and scope of application, achievements when used in the course of independent work. The rating of open educational resources is presented in tabular form according to the Google Play Store for the following countries: Kazakhstan, Great Britain, France, USA, Germany, Italy, Spain, Canada, Mexico, Japan, Egypt, and Malaysia. The platforms for teaching students that are actively gaining popularity were also considered: Coursera, TED-Ed, EdApp, Udemy, WizIQ, Blackboard Learn. Conclusions. This study allows for an examination of the modern educational paradigm by comparing different countries worldwide. It highlights the development of Internet technologies in the face of globalisation challenges and provides insights into the main strategies for enhancing the effectiveness of the educational process through the implementation of Internet technologies and new methodological innovations. Keywords: web resource; online training; conventional methods; courses; professional and communicative competence
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".