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Record W3116682168 · doi:10.5539/jel.v10n1p47

Understanding the Relationship Between Students’ Mathematics Anxiety Levels and Mathematics Performances at the Foundation Level

2020· article· en· W3116682168 on OpenAlexvenueno aff
Emmerline Shelda Siaw, George Tan Geok Shim, Farah Liyana Azizan, Norhunaini Mohd Shaipullah

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
FundersUniversiti Malaysia Sarawak
KeywordsMathematical anxietyMathematics educationFeelingClass (philosophy)AnxietyPsychologyFoundation (evidence)Data collectionTest anxietyMathematicsSocial psychologyComputer science

Abstract

fetched live from OpenAlex

For many students, mathematics is regarded as a challenging subject to learn and master in class. One of the significant factors contributing to the students’ difficulties in learning mathematics is caused by a phenomenon called mathematics anxiety. Mathematics anxiety is a feeling of unease and anxiety toward mathematics and it can be different from person-to-person. Understanding the effects of mathematics anxiety levels on students’ mathematics performances in class can be the key to help students’ mastery of mathematics. The aim of the study is to investigate the relationship between mathematics anxiety levels and students’ mathematics performances at the foundation level. A sample of 545 students from a local foundation centre was chosen for this study. Data collection via questionnaire was used where quantitative data were analysed using correlation, t-test, and descriptive analyses. The results showed that there was a weak positive correlation between students’ anxiety levels and the students’ mathematics performance in their final examination. Recommendations and future potential for this study were further discussed in this paper.

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)0.002
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 score0.064
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.381
GPT teacher head0.430
Teacher spread0.049 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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