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Record W2076172571 · doi:10.5539/ies.v6n11p1

Barriers to Mathematics Achievement in Brunei Secondary School Students: Insights into the Roles of Mathematics Anxiety, Self-Esteem, Proactive Coping, and Test Stress

2013· article· en· W2076172571 on OpenAlexvenueno aff
Malai Hayati Sheikh Hamid, Masitah Shahrıll, Rohani Matzin, Salwa Mahalle, Lawrence Mundia

Bibliographic record

VenueInternational Education Studies · 2013
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
Fundersnot available
KeywordsCoping (psychology)Test anxietySelf-esteemMathematical anxietyAnxietyPsychologyPsychological interventionAcademic achievementSelf-conceptMathematics educationDevelopmental psychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The cross-sectional field survey examined the roles of mathematics anxiety, self-esteem, proactive coping, and test stress in mathematics achievement among 204 (151 females) randomly selected Year 8-10 Brunei secondary school students. The negative dimensions of mathematics anxiety, self-esteem, and proactive coping correlated negatively with mathematics achievement and were both poor predictors of and barriers to mathematics achievement. Both test stress components (intrusive and avoidance) also related negatively with mathematics grades and were poor predictors of mathematics achievement. In addition, females scored significantly higher on negative self-esteem and intrusive stress variables than males. Furthermore, mathematically less able students scored significantly higher on the negative mathematics anxiety and negative self-esteem domains than more able peers. Moreover, positive proactive coping was a good predictor of mathematics achievement. Overall, the findings have practical significance indicating psychological areas where attention, counselling efforts and educational interventions need to be directed to help the at-risk and vulnerable students.

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)0.000
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 score0.148
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.374
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 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

Citations55
Published2013
Admission routes1
Has abstractyes

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