Influence of Social Media Usage on Science Students’ Academic Achievement and Behaviour in Two School-Types in Nigeria
Bibliographic record
Abstract
Students’ achievements and behavior have continued to dwindle over the years and are getting worse in Nigeria with advancements in technology. Both Public and Private school students who have access to Android phones for social networking are spending less time studying after school. This paper was carried out to find out how the time spent on social media can influence the basic science achievement and behavior of secondary school students from two school types. The study sample consisted of 180 junior secondary schools three students of Delta State public and private schools who had access to an Android phone for social media activities after school. Four research hypotheses guided this study. The research instruments used were the social media time questionnaire (SMTQ) with a reliability coefficient of 0.66 and the 2020/2021 academic session results of the students. Access to their results made it possible to compare their basic science and behavioral achievements. The data contained no significant outliers (p=0.054 Kolmogorov-Smirnov- normality test). Data analysis was done using descriptive, t-test, and 2-way ANOVA statistics. Results showed differences in Basic science achievement in favor of private schools and differences in behavior assessment in favor of public schools but the differences were not significant (p= 0.242; p= 0.656). No significant interactions were found between social media usage time and school type on students’ behavior [p=0.470] and basic science achievement [p= 0.549]. Major recommendations are the emphasis on a reduction in social media usage times irrespective of the school type and an increase in study times.
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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".