The Relationship between Socio-Economic Status, General Language Learning Outcome, and Beliefs about Language Learning
Bibliographic record
Abstract
The objective of this study is to explore the probable relationship between Iranian students’ socioeconomic status, general language learning outcome, and their beliefs about language learning. To this end, 350 postgraduate students, doing English for specific courses at Islamic Azad University of Neyshabur participated in this study. They were grouped in terms of their socioeconomic status. They answered a questionnaire in which they indicated their beliefs about language learning in different contexts of language use. Besides, a general language test of proficiency (a Practice test of a TOEFL Test) was administered to all the participants to homogenize them in terms of general language proficiency or general language learning outcome. The quantitative data were subjected to a set of parametric statistical analyses, including descriptive statistics and factor analysis. The findings manifested a positive relationship between the students’ economic status and general language learning outcome. Besides, the findings manifested a significant relationship between the participants’ language learning outcome and their beliefs about language learning. The findings suggest if language instructors are equipped with the necessary information to assist language learners in coping with their negative beliefs, the process of language learning is not only accelerated, but also probable measurement errors may decrease.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".