The Effects of TikTok Application on the Improvement of EFL Students’ English-Speaking Skills
Bibliographic record
Abstract
Technology development and the COVID-19 pandemic altered teaching and learning processes. TikTok, a social media platform that integrates short videos, became a medium of instruction to help students master communication skills, in particular listening and speaking skills. This study, using mixed-methods research, investigated the effects of using the TikTok application on EFL students’ speaking skills and the students’ perceptions toward the use of the TikTok application to improve their speaking skills. Speaking tests and questionnaires were administered to 60 students enrolled in a public speaking class. Additionally, 13 students volunteered to take part in semi-structured interviews. The results showed that TikTok was effective in improving EFL students’ English-speaking skills. Moreover, students had positive perceptions towards the TikTok application. Most students agreed that utilizing TikTok is enjoyable and promotes creativity, and provides new opportunities to learn English. TikTok should be integrated into language learning contexts to make the classroom environment more engaging and promote students’ language proficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".