YouTube and TikTok Videos as Emerging Digital Literacies in Online Teaching: A Must Already in the Wake of COVID-19?
Bibliographic record
Abstract
This quantitative study examines the use of YouTube and TikTok videos as short-form digital resources in online foreign language instruction. While the global shift to online education during the COVID-19 pandemic underscored the importance of accessible and engaging digital resources, the specific pedagogical value of social media platforms still remains obscure. As a contribution to the gap in the literature, a 27-item Likert-type scale, refined from an initial 39-item pool, was administered to 257 undergraduates (162 female, 95 male) enrolled in the Department of English Language and Literature at a state university. The instrument assessed dimensions including motivation, comprehension, intercultural competence, collaborative learning, distraction, anxiety, and practicality. Descriptive and inferential analyses were performed, including frequency distributions and reliability testing (Cronbach’s α = .71). The findings indicated that 61.4% of participants valued the platforms for enhancing engagement, while more than half reported improvements in comprehension and peer interaction. In contrast, relatively few students identified drawbacks: 26–27% cited distraction or impracticality, and 19% reported anxiety. These results demonstrate that although reservations exist, positive evaluations of YouTube and TikTok outweigh negative experiences in the context investigated. The study highlights the dual nature of short-form video integration in online education: strong potential for motivation and comprehension, balanced against limited but noteworthy concerns related to focus and sustainability. The distribution of responses suggests a consistent pattern, with advantages dominating the data while criticisms remain in the minority.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".