Enhancing Speaking Skills and Vocabulary in the EAL Classroom Through TikTok: A Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it