Utilization of Social Media Among Undergraduate Students of the University of Ilorin, Ilorin, Kwara State, Nigeria
Bibliographic record
Abstract
This study examined the utilization of social media and its effects on academic performance among undergraduate students at the University of Ilorin, Kwara State, Nigeria. A descriptive cross-sectional design was employed, and data were collected from 419 students using a structured, self-administered questionnaire. Data were analyzed using SPSS version 25 and summarized with descriptive and inferential statistics. Findings revealed that most respondents (66.7%) were aged between 18 and 23 years, with males (55.6%) slightly more than females. The majority (82.1%) were of Yoruba ethnicity, and most were in their 400-level of study. The majority were active social media users, with 59.7% spending more than seven hours daily and 70.6% accessing platforms primarily through mobile phones. WhatsApp, Facebook, and Instagram were the most frequently used platforms. Perceptions of social media were largely positive: over 85% reported that social media improved their academic performance, enhanced collaboration, connected them with mentors, and provided access to academic tools and opportunities. However, a similarly high proportion reported distractions and reduced concentration during study and lectures. Chi-square analysis demonstrated a statistically significant association between the number of hours spent on social media and students’ perceived academic impact (χ² = 26.49, df = 4, p < 0.001), with heavier users more likely to report negative outcomes. Although social media supports communication and academic engagement, excessive use may hinder productivity and focus. The study recommends that students regulate their usage of social media channels, prioritize academic use of social platforms, and receive guidance on effective digital habits.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".