Causes and Consequences of Examination Malpractice among Senior Secondary School Students in Eti-Osa L.G.A. of Lagos State, Nigeria
Bibliographic record
Abstract
This study investigated the causes and consequences of examination malpractice among senior secondary school students in Eti-Osa Local Government Area of Lagos State, Nigeria. A descriptive survey research method was used for the study as was a simple random sampling technique to select 540 students from twenty selected secondary schools that supplied information to the questionnaire tagged Causes and Consequences of Examination Malpractice Questionnaire (CCEMQ). The instrument was validated by experts in the Department of Social Sciences Education of University of Ilorin. The reliability of the instrument was determined using test re-test method. A reliability coefficient of 0.74 was obtained. The instrument was analysed using percentages and t-test statistics were used to test the hypotheses at a 0.05 level of significance. The findings of the study revealed that the major cause of examination malpractice was sexual harassment by teachers and the main consequence of examination malpractice was that it deprived innocent students’ the opportunity for admission. Findings also revealed that there was no significant difference in the causes of examination malpractice based on gender and age. Based on the results, the researcher recommended that sound educational policy should be put in place with the de-emphasis on the supremacy of certificates over skills and professional competence. There should be improvement in the delivery of instruction especially from the foundational level to the secondary level. Also, stakeholders should stop leap services to examination malpractice.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".