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Record W2012655556 · doi:10.5539/jel.v3n1p40

Parental Involvement in Curriculum Implementation as Perceived by Nigeria Secondary School Principals

2014· article· en· W2012655556 on OpenAlexvenueno aff
Eyiuche Ifeoma Olibie

Bibliographic record

VenueJournal of Education and Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPsychologyAutonomyLikert scaleScale (ratio)PedagogyMedical educationMathematics educationDevelopmental psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the extent to which parents are involved in curriculum implementation insecondary schools in Anambra State of Nigeria as perceived by male and female principals. A research questionand a hypothesis were formulated to guide the study. The survey research design was used. Participants were 257secondary school principals. A 16-item researcher-developed questionnaire, designed on a 4-point scale, wasused to collect data. Structured interviews were also used. Mean scores, content analysis, frequencies and t-testwere used for data analysis. Findings indicated that in the opinions of male and female principals, there was alittle extent of parental involvement in curriculum implementation. This indicates that irrespective gender, theprincipals in this study held a similar view of a little extent of parental involvement to curriculumimplementation in schools. For this trend to improve, school principals and teachers need to make efforts forreaching and involving members to support curriculum implementation. They should design and implementstrategies that will result in improved involvement, and at the same time, balance involvement vis-à-viseducators’ professional autonomy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.368
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2014
Admission routes1
Has abstractyes

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