Relación entre el desinterés estudiantil y el rendimiento académico en matemáticas, bachillerato técnico de contabilidad
Bibliographic record
Abstract
The research addresses the relationship between student disinterest and the academic performance of technical high school students of the "San Juan" Educational Unit, located in the canton of Pueblo Viejo, province of Los Ríos, Ecuador. In this institution, there has been evidence of low performance in the specialization of accounting, particularly in the subject of mathematics, which motivated the present study. The main objective was to determine the level of relationship between disinterest and academic performance in mathematics of students of technical baccalaureate in accounting during the first quarter of the 2024-2025 period. The research was developed under a quantitative-qualitative approach and with a cross-sectional design. It was based on a pertinent theoretical base, applying the survey technique and using a questionnaire as an instrument. The sample was census-based, made up of all students of the specialty. The results obtained show that there is a significant relationship between the variables, which manifests itself in an inverse way: when disinterest increases, academic performance decreases, and vice versa. In this sense, teacher participation through pedagogical strategies aimed at arousing interest becomes decisive to strengthen the skills, abilities and integral development of the students
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".