The Ways of Developing Basic Competences in the Study of Foreign Languages through Interactive Methods
Bibliographic record
Abstract
The search for productive methods of learning foreign languages is urgent for increasing communication needs of society due to globalization. The aim of the research was to find, introduce into the educational process and test the productivity of interactive methods that can be used in the study of foreign languages and the development of basic competencies. The study involved theoretical and empirical methods, pedagogical experiment and observation. The ADDIE (Analysis, Design, Development, Implementation, Evaluation) model was also used in the research. Linguodidactic tests were used to assess the skills acquired by students, which are part of the basic competencies. Mathematical methods for processing experimental data, Pearson’s criterion and Cohen’s coefficient were also used. The following were chosen among the 20 interactive methods: project method, discussion, conversation, collective analysis of the situation, role play, work in pairs, work in small groups and the use of mobile applications. Their effectiveness was investigated in learning the following foreign languages: English, German, French, Spanish, Polish. It was found that they have a positive effect on improving the correctness of language, its purity, clarity, accuracy, logic, expressiveness, conciseness. They also contribute to the development of skills to structure the report, analyse information, formulate and voice opinions, use language tools. Listening and reading improve the results, they develop the ability to correctly answer questions, express oneself in accordance with the given topic etc. Further research can be focused on identifying and developing new effective interactive teaching methods for development of foreign language competencies in students.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".