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Record W4297540773 · doi:10.5539/jel.v11n6p62

Relation Between Mathematical Proof Problem Solving, Math Anxiety, Self-Efficacy, Learning Engagement, and Backward Reasoning

2022· article· en· W4297540773 on OpenAlexvenueno aff
Yuno Shimizu

Bibliographic record

VenueJournal of Education and Learning · 2022
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
Fundersnot available
KeywordsProof of conceptCognitionMathematical anxietyAnxietyPsychologyMathematical proofComputer-assisted proofSimilarity (geometry)Mathematics educationMathematicsComputer scienceImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

Providing adequate proof is one essential mathematical method, but it is a difficult task for many school-age students. While previous studies have revealed the cognitive factors of proving the proof, the function of affective factors and learning engagement has been largely unexplored. This study examined the effects and processes associated with math anxiety, self-efficacy, engagement, and backward reasoning on the provision of proof when solving math problems about geometric similarity. A survey was conducted on 160 junior high school students, from which it was found that: (a) self-efficacy was indirectly and directly positively associated with the provision of proof when solving math problems and was mediated by math anxiety and backward reasoning; (b) math anxiety was indirectly and negatively associated with the provision of proof when solving difficult proof problems and was mediated by backward reasoning; (c) the use of backward reasoning was positively associated with the provision of proof when solving proof problems; (d) cognitive engagement was indirectly and positively associated with the provision of proof when solving proof problems and was mediated by self-efficacy and backward reasoning. The results suggest that improving cognitive engagement, self-efficacy, and backward reasoning may be effective in the provision of proof when solving proof problems.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 integrity0.0000.001
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 head0.028
GPT teacher head0.308
Teacher spread0.280 · 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 designTheoretical or conceptual
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

Citations6
Published2022
Admission routes1
Has abstractyes

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