Characteristics of the Influence of Digital Technologies on the System of Learning a Foreign Language
Bibliographic record
Abstract
A foreign language is a subject that involves the creation of an artificial language environment for students, which predetermines the variable inclusion of various digital learning tools in new perspectives for teaching a foreign language. The main purpose of the study is to determine the features of the influence of digital technologies on the system of learning a foreign language. To achieve our goals, we have applied the methodology of functional modelling, which allows us to graphically depict how the process of learning a foreign language can be improved through the use of digital technologies. The world community is gradually but surely moving towards Industry 4.0, which brings new opportunities for various everyday processes. Globalization is massively trying to introduce English into all types of people's activities, but the study of other languages does not stand still and more and more people are striving to learn new types of foreign languages, which is why the chosen research topic is extremely relevant today. Based on the results of the study, a functional model was formed that demonstrates the process of learning foreign languages through the use of modern digital technologies. The study has limitations and is associated with the impossibility of applying the proposed model outside of one country and all languages. Further research needs to expand the capabilities of the functional model and form elements of flexibility in it for use in the study of foreign languages that are very complex in their structure.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".