Prediction of Academic Achievement of Vocational School Students Based on Tiktok Usage Patterns and Cognitive Styles: Multiple Linear Regression Model (Case Study: SMKS YPIS MAJU)
Bibliographic record
Abstract
This study aimed to predict vocational high school students’ academic achievement by analyzing the influence of TikTok usage patterns and cognitive styles using a multiple linear regression model. The research was conducted at SMKS YPIS Maju Binjai with a sample of 100 students selected through purposive sampling. Data were obtained through questionnaires on TikTok usage patterns and cognitive styles, as well as students’ academic records. TikTok usage (X1) was measured by frequency, duration, and its impact on study habits, while cognitive style (X2) was measured based on visual, verbal, and mixed learning preferences. Academic achievement (Y) was represented by students’ average report card scores. The regression analysis produced the equation Ŷ = 85.869 +(- 0.3495X1) + (0.1993X2). The results showed that TikTok usage had a significant negative effect on academic achievement, whereas cognitive style had a significant positive effect. The model demonstrated good predictive accuracy with R² = 0.392, MAE = 1.77, MSE = 5.26, RMSE = 2.29, and MAPE = 2.16%. This study contributes by integrating social media usage patterns and cognitive factors to predict students’ academic achievement and provides practical insights for educators in guiding students to balance social media use with academic learning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".