Innovative Methods in Language Disciplines During Profile Training Implementation
Bibliographic record
Abstract
The advanced pedagogical experience testifies to the presence of a number of methods in the practice of teaching language disciplines used by teachers during profile training at school or other educational institutions. With the development of modern information technologies, the role of traditional methods of studying language disciplines in the educational process is changing to the advantage of more innovative (interactive) methods of studying language disciplines. Therefore, the purpose of the article is to investigate the peculiarities of the application of innovative methods in language disciplines during profile training implementation. To achieve the purpose specified, an academic paper uses a number of general scientific (method of theoretical analysis, method of observation, method of description and method of synthesis) and empirical (experimental method, modeling method and calculation method) research methods. The practical reflection of the state and dynamics of mastering language disciplines during profile training is demonstrated through: the efficiency level of the applied innovative methods of studying language disciplines; the dynamics of the average number of students, involved in studying foreign language per student at the level of higher secondary education; rating of the countries of European Union and Ukraine on English proficiency according to the Global rating of countries and regions; provisions of New Ukrainian School Concept. It has been established that in the countries of European Union the practice of studying language disciplines is based on the translation method, the method of meaningful and language integrated learning, the methods of using digital technologies and the method of using creative objects. The study has revealed that such countries of European Union, as Luxembourg, Romania and Finland have significantly high levels of foreign language proficiency; however, such levels of language command are significantly low in Greece, Denmark, Spain, Germany and Portugal. It has been established that Ukraine is classified as a country with a low level of English language command (proficiency) in the Global Ranking of Countries and Regions (according to data of 2019, 2017 and 2016) and a moderate level of English language proficiency (according to data of 2018 and 2015). The following types of innovative methods, currently used by teachers in language disciplines during profile training, have been summarized, namely: the use of information and computer technology in the process of language learning; the use of developmental methods in the form of dialogue, discussion, seminar or brainstorming; construction of the educational process in the form of a game, project, team game; introduction of creative and interactive tasks into the educational process.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".