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Record W7042826189

Relación entre autoestima, ansiedad y motivación matemáticas en estudiantes de precálculo

2024· article· en· W7042826189 on OpenAlexaboutno aff

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Skills and Education
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyStatistical analysisPrecalculusSample (material)CorrelationSubject (documents)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

This research was focused on exploring the relationship between self-esteem, anxiety and mathematical motivation in precalculus students of the Metropolitan Campus of the Latin American University of Science and Technology (ULACIT in Spanish), during the first quarter of 2024. Possible differences in terms of gender and area of study were analyzed for each of the variables. The methodological approach adopted was of a quantitative descriptive-correlational nature. The sample consisted of 374 pre-calculus students. To carry out the analysis of the hypotheses proposed, Student’s t and Pearson’s correlation statistical techniques were used. The findings revealed that males showed higher levels of anxiety and lower motivation towards mathematics than females. No significant differences in anxiety, mathematical motivation, and self-esteem were identified according to the area of study. A correlation was observed between the variables: as mathematics anxiety decreased, self-esteem and motivation towards the subject increased. In addition, an increment in mathematical motivation was related to an increase in self-esteem. The study showed that 22.46% of the students presented high levels of mathematical anxiety, while more than 55% showed high levels of self-esteem and mathematical motivation.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.306
Teacher spread0.297 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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