Social Media Applications as an International Tool for the Development of English-Language Communicative Competencies
Bibliographic record
Abstract
The aim of the article is to determine the effectiveness of various international tools designed to develop students’ English-language communicative competencies. Several scientific methods were used: observation, testing, experimental training etc. Statistical processing of the data obtained during pedagogical experiment was carried out, visualization with the use of graphic method is applied. The description and verbal recording of the results of the study confirm its effectiveness. During the research, experimental training was carried out with the use of social media applications for the development of foreign language communicative competence in second-year students of groups G1, G2. Observations and testing of students while developing foreign language communicative competence were carried out while writing e-mails and creating videos for TikTok in English. The educational platforms, programmes, Internet resources were also used in accordance with the goals and topics of training sessions. The practical results prove the advantages of certain social media applications in the acquisition of English-language components of communicative competences (CCC). G1 students demonstrated a standard procedure for acquiring a linguistic, socio-cultural CCC with a “lag” of the regional geography component. In G2 group, the higher levels of CCC were observed in most cases. Mostly positive markers of Communicative Competences were demonstrated at the medium (344.1 points, 69.8% of G1 respondents) and a sufficient level (371.0 points, 75.8% of G2 respondents). Promising “growth points” were identified. The directions concerning further developments of progressive methods are highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| 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".