Formation of Lexical Competence in Applicants for Education at Distance Learning (Experience of Foreign Scientists)
Bibliographic record
Abstract
The modern peculiarities of the formation of lexical competence in students in distance education are quite acute due to the alternative organization of the scientific process and the possibility of its improvement in modern educational programs. The use of digital technologies in the learning process can be useful from the point of view of forming lexical competence and their further development in the context of strengthening the role of distance education in the modern educational program. The purpose of the article is to study the formation of lexical competence in students in distance learning, as well as the use of effective digital platforms and information technologies that can improve the components of lexical competence, such as terminology, word formation, the ability to build lexical and semantic constructions, etc. The main objective of the study is to analyze the theoretical and methodological aspects of lexical competence development in students in the context of global digitalization. The article focuses on current trends in the development of distance education and its role in the further educational process. The key prospects for development and possible ways to improve the formation of lexical competence of students studying in a distance format are outlined. Useful means of development and features of lexical competence formation are proposed, its theoretical concept is studied and its structural components are characterized. The obtained results of the study can be useful for improving the quality of the educational process in educational institutions and can be used for further development of education in the modern world.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".