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Record W2786741680 · doi:10.21083/ajote.v7i1.3904

Science Curiosity as a Correlate of Academic Performance in Mathematics Education: Insights from Nigerian Higher Education

2018· article· en· W2786741680 on OpenAlexvenueno aff
Benjamin Ogbole Abakpa, Joshua Abah Abah, Abel Okoh Agbo-Egwu

Bibliographic record

VenueAfrican Journal of Teacher Education · 2018
Typearticle
Languageen
FieldPsychology
TopicPsychological and Educational Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCuriosityMathematics educationPsychologyInstitutionScale (ratio)PedagogySocial psychologySocial scienceSociologyGeography

Abstract

fetched live from OpenAlex

This study investigated the relationship between the science curiosity levels of undergraduate of mathematics education in a Nigerian higher educational institution and their academic grade point averages. The study employed a correlational survey research design on a random sample of 104 mathematics education students. The Science Curiosity Scale – Comparative Self Report was adapted to measure the students’ distinctive appetite for consuming science-related media for personal edification. The correlational analysis of science curiosity scores and the students CGPA indicated a weak negative relationship (r = -0.049, p = 0.621), suggesting an interplay of other important factors in the relationship between academic performance and science curiosity. Based on the findings of this study, it was recommended that key stakeholders of mathematics education consider curiosity as a complex ability related to several functions of the mind and thus enhances systematic commitment on the part of the learner.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.409
Teacher spread0.352 · 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 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

Citations9
Published2018
Admission routes1
Has abstractyes

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