TikTok or Not TikTok: Students’ TikTok Utilization and Academic Engagement
Bibliographic record
Abstract
Background: As of 2024, TikTok is being investigated in the United States for societal harm apart from other possible harms. This research thoroughly examines the students’ TikTok utilization, their academic involvement and relationship between those two. The complex aspects driving TikTok engagement and its impact on academic performance have been analyzed through correlation analysis by the data collected from university students in the United States. Findings: Our research highlights that the student population is aware of the potential harm of social media, TikTok in this case. Besides, the results reveal a negative association between students’ self-reported TikTok utilization and their academic success. While students are aware of the potential harms of social media, factors like frequent checking behaviors and prioritization of academic goals subtly influence their TikTok engagement. This highlights the complex interplay between personal choices and academic obligations. Implications: Our findings underscore the critical need for early interventions to promote responsible TikTok usage and support academic achievement in the digital age. This information will assist educators in developing more proactive action plans and creating awareness for TikTok usage harms that will improve the well-being of students at the early stages of education. Limitations: Although our study offers new insights, it still needs more investigation due to its limitations, which include its reliance on self-reported surveys.
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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 | 0.003 | 0.021 |
| 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 | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".