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Record W4281737025 · doi:10.1111/nyas.14805

Appraisals of previous math experiences play an important role in math anxiety

2022· article· en· W4281737025 on OpenAlexaff
Thomas E. Hunt, Erin A. Maloney

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMathematical anxietyAnxietyPsychologyMathematics educationDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Math anxiety affects many people, from young children through to older adults. While there has been debate concerning the developmental trajectory of math anxiety and negative math attitudes, little attention has been given to the role of appraisals of previous math experiences. We surveyed 308 adults (mean age = 27.56 years, SD = 11.25) and assessed self-reported measures of math anxiety, mathematical resilience, math attitudes, and appraisal of previous math experiences. As hypothesized, all variables were found to be interrelated. Math anxiety was significantly negatively related to appraisal of previous math experiences, mathematical resilience, and math attitudes. Moreover, appraisal of previous math experiences was shown to mediate the relations between (1) math anxiety and math attitudes, and (2) mathematical resilience and math attitudes. The findings demonstrate the importance of considering current appraisals of previous math experiences and are consistent with an interpretation account of math anxiety. This may help inform cognitive-based interventions that focus on one's interpretation of past events to support current and future math learning and engagement.

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)0.003
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 score0.051
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0020.000
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 head0.084
GPT teacher head0.400
Teacher spread0.315 · 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

Citations24
Published2022
Admission routes1
Has abstractyes

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