Exploring the Impacts of TikTok on the Academic Performance of Chinese Secondary School Students
Bibliographic record
Abstract
Since TikTok was released in 2016, more and more people have found TikTok interesting and have tried to become users. TikTok contains many features, such as video, chat, learning, and working. People can relax and have fun in their spare time with TikTok. Nevertheless, as TikTok has become increasingly popular, more students are becoming the primary users of TikTok. At the same time, the variety of short videos available on TikTok can lead to inconsistent content quality due to their low cost of production. As instructors, schoolteachers must know how students are affected when watching TikTok. After literature review, this paper mainly found the four areas of influence from TikTok that students will experience during the emergence phase: psychological influence, physical influence, behavioral influence, and positive influence. These four areas of influence indicate how instructors should properly guide students in using TikTok, which will provide references for future instructors and students in the education area.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".