Age Factor in Business Education Students’ Use of Social Networking Sites in Tertiary Institutions in Anambra State, Nigeria
Bibliographic record
Abstract
There are diverse social networking sites which range from those that provide social sharing and interaction to those that provide networks for professionals within same and other fields. Social networking sites require a user to sign up, create a profile and begin sending short messages about what the user is doing or thinking. The study sought to establish from business education students in tertiary institutions in Anambra state Nigeria, hours of the day spent on social networking sites, social networking sited used and what they use social networking site for. Relevant theories and literature were reviewed. Population of the study was made up of 577 penultimate and final year business education students of four tertiary institutions in Anambra state. Proportionate sampling technique was used to select 236 students for the study. Three hypotheses guided the study. A structured questionnaire was used to gather information for the study. The data collection instrument was subjected to a reliability test which yielded a reliability coefficient of 0.80 using Cronbach alpha. Analysis of Variance (ANOVA) was used to test the hypotheses at 95% confidence interval. Findings revealed that business education students differed significantly on hours of the day spent using social networking sites and on what they use social networking sites for, but do not differ significantly on social networking sites they use as a result of age. Consequently, it was recommended, among others, that business education students of all age brackets should be taught to harness the educational potentials of social networking sites so as to effectively use these sites for educational purposes.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".