Social Networking Addiction and Quality of Academic Life among First-Year High School Students in Saudi Arabia: The Mediating Role of Academic Procrastination
Bibliographic record
Abstract
Students with high levels of procrastination were unable to organize and achieve their academic goals. A student who procrastinates may face internal consequences such as low academic performance, bad learning habits, and low learning motivation. When he/she is unable to address procrastination, this will hinder his/her academic performance. The study group of the research consists of 258 boys studying in high schools in the Riyadh region in the 2019-2020 academic year. They aged 16-18 years, (M= 17.23,SD= 4.45). Social Media Addiction Scale - Student Form, High-School Satisfaction Scale, and Tuckman's procrastination scale –short form were used for gathering and analyzing data. Quality of academic life correlates negatively with social networking addiction and academic procrastination. On the other hand, social networking addiction was found to be positively correlated with academic procrastination. Regression coefficients of the empirical model show that social networking addiction had a direct negative effect on the quality of academic life (b= -0.49, p<0.001) and a direct positive effect on academic procrastination (b=0.52, p<0.001). Results support previous research showing that social networking addiction negatively impacts academic achievement by creating academic procrastination, reducing sleep quality, and increasing academic stress. Evidence indicates a positive and significant correlation between inappropriate and problematic use of technology and quality of academic life. Procrastination may interrupt the academic performance, as procrastinators are likely to avoid completing the task at Hand until the last moment. They may also be unable to invest the time and effort necessary for performing as they underestimate the amount of time required for completing specific tasks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".