The Impact of Cross-Cultural Background Knowledge upon Iranian EFL Students’ Productive Skills
Bibliographic record
Abstract
Teaching culture has been considered important in foreign language instruction for almost a century. In this study theliterature behind Cultural Schema Theory was reviewed and the practicality of applying this theory into the EFLclassroom and its effect on the EFL students’ productive skills (writing and speaking) were observed. This studyincludes two phases. First, a learning phase in which the treatment group was exposed to a variety of materials ofdifferent cultural issues. Second, a test phase in which a pre and post-test of speaking and writing were given to bothcontrol and experimental groups to determine any differences in their writing and speaking performance. And at theend of the treatment a semi-structured interview was used to know the experimental group’s opinion about differentcultural issues that were discussed during the term in their EFL class. The data was subjected to the statisticalprocedure of t-test and the results of this study indicated that not only all the students of experimental groupoutperformed the students of control group in their speaking and writing performance but also according to thesemi-structure interview the students of experimental group had a positive reaction towards the integration of cultureinto the classroom context. The results showed that L2 learners may need to understand different aspects of the targetculture better in order not only to speak and write accurately but also to interpret appropriately what they hear and tointeract effectively with members of the target culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".