Attribution Theories in Language Learning Motivation: Success in Vocational English for Hospitality Students
Bibliographic record
Abstract
One of the overlooked motivational areas for VET hospitality students learning English is attribution theories, students’ beliefs about why they fail or succeed. Weiner identified four basic attributions that people tend to have in achievement situations (2010; 1984): ability, effort, task difficulty, and luck, which contribute to students’ motivation to study. With the aim of researching motivation in order to prevent program abandonment, which is high in Spain, this 2-phased study examined attribution theories for a group of 51 adult, English for hospitality students studying in vocational courses offered by the public employment agency in Extremadura, Spain. It found that in general students’ attribution theories were mostly negative, though they strongly indicated that they could improve through effort. These results may be associated with students’ perception of the instructor and course and the social, dynamic nature of students’ beliefs in general as they are formulated in situ. Suggestions are made for incorporating this possible influence into future vocational course visions for English for hospitality 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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".