Enhancing Voter Education Knowledge of Adolescents Through Social Interaction Instructional Models
Bibliographic record
Abstract
The study determined the effects of group investigation and jurisprudential inquiry of social interaction instructional models on adolescents’ knowledge of voter education related contents in senior secondary school Government curriculum. The ultimate aim was to curb voter apathy among young citizens. The design was Quasi-experimental non-equivalent pre-test, post-test control group design. The sample for the study consisted of 165 SS II students drawn from public secondary schools in Nsukka Local Government Area of Enugu state Nigeria. Using a multi-stage sampling technique, the intact classes from the schools were assigned to the two experimental groups. Data collected using Multiple Choice Government Achievement Test (MCGAT) were analyzed employing mean and standard deviation for the research questions and ANCOVA for testing the hypothesis at P < 0.05 level of significance. The findings showed that group investigation and jurisprudential inquiry models enhanced students’ acquisition of knowledge in voter education related contents; although group investigation seemed more efficacious. There was also a statistical difference in the mean achievement scores of students with group investigation performing significantly better. These findings were exhaustively discussed with the far-reaching recommendations on how to improve voter education knowledge and potentials of young ones as future adult citizens.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".