Motivation and English Achievement in Mexican Teacher Students: A Correlation Study
Bibliographic record
Abstract
The study aimed to measure the correlation between the motivation to learn English as a foreign language and the achievement level in students in the first semester of the Bachelor’s degree in Primary Education at a higher education school “Escuela Normal Oficial de Irapuato” (ENOI). These future teachers of basic education are of interest due to the high impact they have on the education of future generations in Mexico. The present study comprised a quantitative non-experimental correlational research. Gardner´s attitude/motivation test battery (Gardner, 1985) was used to measure motivation and the Oxford on-line Placement Test scored the students’ English level achievement. The motivation results show the high motivation intensity these future teachers have to acquire English as a foreign language, both for integrative and instrumental reasons; the average is 5.79 points out of 7. The English achievement results showed that 67% of incoming students barely reached an A1 which is less than 20 points out of 120, even though, it did not include a speaking section which has been reported as one of the most difficult abilities. The Spearman’s rank correlation was calculated at 0.2430 using the results before mentioned. Based on these grounded findings, the authors propose focusing on specific learning areas that can better prepare these futures teachers to take advantage of their own students’ crucial period.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".