Facebook, Twitter Activities Sites, Location and Students’ Interest in Learning
Bibliographic record
Abstract
This study was carried out to ascertain the influence of social networking sites activities (twitter and Facebook) on secondary school students’ interest in learningIt also considered the impact of these social networking sites activities on location of the students. Two research questions and two null hypotheses guided the study. Mean and Standard Deviation were used to answer the research questions, while t-test statistics was applied in testing the null hypotheses. In carrying out the study, the researchers adopted Ex-post Facto research design. The sample of the study consisted of 240 senior secondary school Two (SSS11) students from public schools. Multi-stage sampling technique was used to select 120 students from the urban and 120 students from the rural areas. A 15-item Questionnaire was used in data collection. Cronbach Alpha was used to establish the internal consistency of the instruments. Cronbach Alpha Coefficients values of 0.88 and 0.72 were obtained. The findings of the study indicated that there is a significant influence of students’ Twitter and Facebook activities over their interest in learning. Students in urban schools had higher Mean Score interest in learning than those in rural location. The result also indicated that there is no significant difference in the Mean interest ratings of urban and rural students.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".