Using TikTok as a Tool for English Vocabulary Learning in the EFL Context
Bibliographic record
Abstract
One of the most crucial components of studying a language is learning its vocabulary. Without sufficient vocabulary, communication becomes problematic. However, research shows that Saudi Arabian university students have a small English vocabulary size. Many technological tools exist in the literature for increasing students’ vocabulary size, including TikTok videos. TikTok has become one of the most popular social media platforms for producing and sharing videos. Because students’ perception of tools used in the classroom determine the tools’ success, investigating their perception of using TikTok videos for English vocabulary learning is essential. In this study, a quantitative research design was used to investigate students’ perception toward using TikTok as an English vocabulary learning tool in the EFL context. A descriptive quantitative methodology was adopted to analyze data from 115 female students at King Abdulaziz University. According to the findings, most students have a positive perception toward the use of TikTok videos as a vocabulary learning tool. These findings suggest that TikTok videos can be used to increase students’ English vocabulary size. The current study proposes an alternative pedagogical means for teaching English vocabulary to language teachers who want to help their students expand and develop their vocabulary.
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 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".