Organization of Distance Learning in Google Meet in Modern Conditions
Bibliographic record
Abstract
The aim of the article is to analyze the key aspects of distance learning organization on the Google Meet platform through the prism of modern digital educational trends and globalization challenges. The study is based on pedagogical knowledge theoretical methods (analysis and synthesis), dialectical and systematic method, as well as on the SWOT-analysis opportunities. In results it is underscored several key strengths, including user-friendly features, robust security measures backed by Google Corporation, an accessible interface, and valuable functionalities such as the digital whiteboard application that empowers educators to illustrate educational content and live subtitles that enhance inclusivity. However, it is worth noting that Google Meet's compatibility issues with certain devices pose potential challenges in educational settings. Addressing these limitations presents an opportunity to ensure the continued effectiveness of Google Meet as the primary digital conduit for distance learning. The scientific novelty of the study reveals that the platform offers distinct advantages and notable drawbacks, all of which merit careful consideration when planning educational activities. For example, vulnerabilities are the difficulties in launching on certain mobile devices, the limitation of the free version. The conclusions summarize the results of the research and note that the important advantages of the service are the support of the Google Corporation, the accessibility of the interface and the convenience of using digital tools. It is proposed to further develop the integration of the service with the company's products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".