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Record W1504315326 · doi:10.5430/wje.v5n3p91

Counselling Strategies for Curbing Examination Malpractices in Secondary Schools in Enugu State, Nigeria

2015· article· en· W1504315326 on OpenAlexvenueno aff
Anthonia Chinonyelum Egbo

Bibliographic record

VenueWorld Journal of Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaMalpracticeStratified samplingMedical educationPsychologySample (material)Family medicineMedicinePolitical scienceClinical psychologyPsychometricsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.420
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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