Enhancing Speaking Skills and Vocabulary in the EAL Classroom Through TikTok: A Literature Review
Bibliographic record
Abstract
The widespread adoption of TikTok globally has positively impacted its application in education, particularly in language teaching and learning. English, being widely spoken as a lingua franca, is extensively used for content dissemination through TikTok worldwide. However, a preliminary search on the internet revealed a need for more research syntheses on the use of this platform in the English as an Additional Language (EAL) classroom. This scarcity prompted the research discussed in this article. The study took the form of a literature review and followed the principles of Systematic Literature Review, aiming to explore how TikTok has been used in the EAL classroom and what learning benefits it offers. An adapted version of the research protocol model developed by Sarah Visintini was employed for searching, selecting, and extracting written productions from web-based databases to compose the research sample. Eight peer-reviewed articles constituted the final sample based on retention and discard criteria. The retained texts were analyzed using the thematic analysis method proposed by Virginia Braun and Victoria Clarke. The findings indicate that TikTok can effectively enhance speaking skills and expand the vocabulary repertoire of EAL students. Moreover, its usage can aid in maintaining student focus on classroom activities. Further comprehensive searches in online databases, using diverse mechanisms, can yield substantial corpora, facilitating broader and more in-depth analyses and discussions on the pedagogical applications and benefits of TikTok in the EAL classroom.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".