Social Media as a Development Tool English Communicative Competence
Bibliographic record
Abstract
The purpose of the research is to determine the effectiveness of the practical application of social media based on Web 2.0 technologies, aimed at developing students' English-speaking communicative competence. To implement the tasks and test the hypothesis put forward, the following scientific research methods were used: empirical (experimental learning using social media based on Web 2.0); diagnostic: observation, testing of students; statistical method - mathematical analysis of the data obtained during the experiment; descriptive: description and verbal recording of results. The results of the experimental study showed the correctness of the hypothesis put forward that the formation of English-speaking communicative competence among students will become more effective when creating a methodology using social media formed on technologies Web 2.0. For practical implementation of obtained theoretical conclusions after the experiment, it is necessary to have certain pedagogical conditions. Among these: taking into account the peculiarities of the educational environment, enhancing the speech activity of students with the participation of social media based on Web 2.0 technologies. They were developed on the basis of the results of diagnostics of the level of formation of the English-speaking communicative competence in the process of teaching students in streaming mode. Future scientific searches are possible in the direction of theoretical substantiation and practical application of new social media based on Web 2.0 technology in other training courses and other (non-philological) specialties. This vector of research is especially necessary during distance learning as an alternative to the traditional 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".