MétaCan
Menu
Back to cohort
Record W4390622424 · doi:10.53555/sfs.v10i1.1870

Redefining Mathematical Jitters: Pioneering A Scale For Children's Anxiety In Numerical Contexts

2023· article· en· W4390622424 on OpenAlexvenueno aff
Meenakshi Dwivedi, Rambabu Singh

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
Fundersnot available
KeywordsMathematical anxietyAnxietyCronbach's alphaFeelingScale (ratio)PsychologyMathematical problemDevelopmental psychologyClinical psychologyPsychometricsMathematics educationSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Mathematical Anxiety is commonly defined as feelings of tension and anxiety that interfere with the manipulation of numbers and the solving of mathematical problems in academic situations and even in daily life. It negatively impacts the academic achievements of children. In this study, a Mathematical Anxiety Scale was developed to evaluate mathematical anxiety among primary school students aged between 8 to 10 years. The scale contains twenty-four items that measure mathematical anxiety in six domains: anxiety in doing mathematical calculations, performing mathematical activities, mathematical thinking, mathematical evaluation and doing everyday mathematical tasks. The validation of the scale was done using the data of 250 girls and 237 boys participants. The frequency analysis using SPSS shows the Mean=41.37, Standard Deviation ( ) =7.01, and Median=40.00 as obtained. The criteria of scores developed are: A score above 54 shows extremely high math anxiety, a score between 46–54 shows high math anxiety while scores 37–45, 29–36, and less than 29 shows moderate math anxiety, low math anxiety, and extremely low math anxiety respectively. The Cronbach Alpha coefficient calculated for the evaluation of the internal consistency was determined as 0.728 and this value shows that the scale had good reliability. With the results of this study, the scale’s content validity was evaluated and it was shown to have acceptable valid features in all.

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)0.001
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 score0.005
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.162
GPT teacher head0.358
Teacher spread0.196 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries SciencesSame topicEducation, Achievement, and GiftednessFrench-language works237,207