The Influence of Students’ Sociocultural Background on the IELTS Speaking Test Preparation Process
Bibliographic record
Abstract
The article is aimed at highlighting the sociocultural factors a teacher/IELTS instructor should consider preparing Russian students for the IELTS exam. The main focus of the study was on four speech functions most frequently used in the IELTS Speaking Test: explaining and paraphrasing, expressing personal opinion, providing personal information, and summarizing. The study aims to question the assumption that the problems arising in the use of these speech functions are provoked by the students’ low language level and to investigate if there are any sociocultural issues connected with the use of the above-mentioned speech functions influencing students’ performance during the IELTS Speaking Test. The study was conducted among first-year students at the Higher School of Economics (HSE) in the Faculty of Computer Science. To see the problem from a different perspective, the study involved not only the first-year students who seem to struggle with the speech functions but also their English teachers who can provide trustworthy first-hand information on the problems the students frequently face. The results of the study demonstrate that the cause of problems students encounter using the speech functions should not be attributed only to their language knowledge, as do the majority of interviewed teachers. The way students tend to explain, paraphrase, summarize, express their opinion and provide personal information is culturally defined which influences students’ ability to perform these functions effectively. To help Russian students avoid sociocultural problems preparing for the IELTS Speaking Test, a teacher/IELTS instructor should aim to increase students’ sociocultural awareness of the pitfalls in the use of the essential speech functions and sociocultural competence in a foreign language.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".