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Record W2997514286

EFFECT OF SCAFFOLDING INSTRUCTIONAL STRATEGIES AND GENDER ON PERFORMANCE OF PUPILS IN BASIC SCIENCE AND TECHNOLOGY IN RIVERS STATE, NIGERIA

2019· article· en· W2997514286 on OpenAlexaff
Princewill Okechukwu Ejekwu, Aniafiok Udo Inyon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsNonprobability samplingNull hypothesisMathematics educationAnalysis of covarianceTest (biology)Quasi-experimentResearch designData collectionPopulationPsychologyMathematicsStatisticsMedicine
DOInot available

Abstract

fetched live from OpenAlex

This study examined the effect of scaffolding instructional strategies and gender on the performance of pupils in Basic Science and Technology in public primary schools in Rivers State. Two specific objectives and two null hypotheses guided the study. Non-randomized pretest, post-test and control group experimental design was adopted for the study. The population size consisted of 42,409 basic four pupils; out of which 147 were drawn as sample size using purposive sampling technique. The instrument for data collection was the Basic Science and Technology Performance Test. The data was analyzed using Analysis of Covariance (ANCOVA). The findings of the study revealed that there is a significant difference in the Basic Science and Technology mean performance scores of pupils taught with scaffolding instructional strategies and those taught with conventional method. The Basic Science and Technology mean performance scores of boys and girls taught with scaffolding instructional strategies does not differ significantly with those taught with conventional method. Recommendations were made which include that scaffolding instructional strategy should be used in classroom teaching/learning interaction to enhance the teaching and learning of pupils as well as improve their performance in all subjects.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.302
Teacher spread0.291 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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