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Record W3011142251 · doi:10.5539/gjhs.v12n5p1

Assessment of the Efficacy of Creativity-Based Instructional Model on Scientific Attitude in Basic Science and Technology Among Pupils

2020· article· en· W3011142251 on OpenAlexvenueno aff
Joy Chioma Orji, Celina Shitnan Gana, Victor Sunday Ezema, Emmanuel C. Okenyi, Christian S. Ugwuanyi, Anthonia N. Ngwoke, Florence Eleje Otta, Alexander Chuwkuemeka Okondugba, Julie U. Ibiam

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPsychologyAnalysis of covarianceMathematics educationData collectionPositive attitudeTest (biology)Null hypothesisScale (ratio)Structural equation modelingSample (material)Social psychologySocial scienceMathematicsSociologyStatistics

Abstract

fetched live from OpenAlex

This study assessed the efficacy of creativity-based instructional model on scientific attitude in Basic science and technology among pupils. The study adopted the quasi-experimental design of non-equivalent control group. A sample of 244 pupils (135 males and 109 females) was drawn for the study using a multistage sampling procedure. The instruments used for data collection was the Scientific Attitude Assessment Scale (SAAS). Data collected were analyzed using mean to answer the research questions and Analysis of Covariance (ANCOVA) to test the null hypotheses at 0.05 level of significance. The findings of the study revealed that creativity-based instructional model significantly enhanced pupils’ scientific attitude. It was equally revealed that gender had no significant influence on pupils’ scientific attitude. Based on the findings and conclusions, it was recommended among others, that the use of Creativity-based Instructional Strategy should be encouraged and popularized among primary school teachers through workshops, conferences and seminars.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.008
Science and technology studies0.0010.013
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.451
Teacher spread0.367 · 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.

Study designObservational
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
Published2020
Admission routes1
Has abstractyes

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