Relación entre autoestima, ansiedad y motivación matemáticas en estudiantes de precálculo
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".