Making Culture Happen in the English Language Classroom
Bibliographic record
Abstract
The issue of introducing the target culture into language classroom practice has long been an object of debates as well as the opinions of the learners towards it. Eventually, modern practitioners found a way of having the language learners acquainted with the target culture and introducing culture through culture-based textbook activities. However, the issue of additional culturally-oriented activities in improving students learning habits is questionable today. The purpose of this paper is to examine their effect and to investigate the attitudes of students towards language teaching and learning through culture-based activities (games, role plays, dialogues, video clips, discussions and comparisons of local and target cultures). The paper presents the results of the study conducted in one of the top universities of Kazakhstan throughout the spring semester of the 2012 academic year. Eighty students of different cultural backgrounds took part in the study. The activities for the experimental groups were modified according the tasks in each unit of one of the contemporary textbooks used in General English lessons. These activities varied from warm-ups to homework tasks in the units accordingly. The results suggest that practice of the various culture-based tasks and exercises helped the students to improve their communicative and linguistic competences in English. The results obtained from this study also offer insights into how culture-based activities can be used to develop and enhance not only students’ language skills but also their awareness of various culture-sensitive issues.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".