Counselling Strategies for Curbing Examination Malpractices in Secondary Schools in Enugu State, Nigeria
Bibliographic record
Abstract
This study investigated the Counselling strategies for curbing “Examination Malpractices” in Secondary Schools inEnugu State Nigeria. The researcher used three research questions. The Design used was a descriptive survey design.Sample consisted of 335 respondents comprising principals (N= 19), PTA secretaries (N =19), teachers (N=276) andCounsellors (N=21) selected through stratified random sampling. A researcher–developed questionnaire containing27 items was validated by experts, subjected to reliability tests using Cronbach Alpha coefficient method to collectthe data. Data was analyzed using mean ratings which revealed that out of the 27 proposed strategies investigated,the respondents agreed that 24 personal/social, educational and teachers’ forum strategies will be useful incounselling students against examination malpractice in secondary schools in Enugu State. This means that unlessseveral social, educational and teacher sensitization strategies are vigorously implemented, School counselling mightnot contribute much in the fight against Examination malpractice as one and evil practice in Secondary EducationSector. Recommendations included that Educational Management bodies, principals and PTA should sponsor andmount campaigns through the use of posters, bulletin boards, seminars and jingles to change student’s attitudetowards examination malpractices.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".