Differential Effects of Instruction Technique and Gender on Secondary School Students’ Achievement in Civic Education in Anambra State, Nigeria
Bibliographic record
Abstract
The essence of civic education is to train a child on how to adapt his or her life to the provisions of the social system to engender equality, social justice and inalienable rights of life, liberty and pursuit of happiness. The current dismal performance of students on civic education has sadly threatened the actualization of lofty ideals of civic education in Nigeria. In search of answers to this growing problem, this study explored the differential effects of group instruction technique (GIT) and gender on secondary school students’ achievement in Civic education in Anambra State, Nigeria. The design of this study was non-randomized control group, pre-test, post-test quasi experimental design while multi-stage sampling procedure was used to sample six coeducation schools from each of the six education zones that make up 258 public secondary schools in Anambra State. The sample was 193 Senior Secondary 2 students drawn from six intact classes. The instrument Civic Achievement Test (CAT) was used for data collection. Data from the study was analyzed using Analysis of Covariance (ANCOVA). The result revealed significant differences in Civic achievement test between groups. The experimental group taught Civic education with the use of GIT recorded higher mean scores than their counterparts in the control group taught with LM. Also, gender was a covariant factor on the differential effect of GIT and gender on students’ achievement in Civic education. Given this empirical evidence; GIT stands as an effective alternative to improve on students’ academic achievement in Civic education. Thus, recommendation was made for its adoption by the stakeholders in education especially the Anambra State Ministry of Education to improve on the status-quo.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".