Developing Communicative Competence: An Evaluative Study of Request and Apology in English Language Textbooks in China
Bibliographic record
Abstract
China’s modernization and sustained economic development have made increasing demands on English language education to produce English learners who can function adequately in all sectors of cross-cultural communication. English major students who are immersed in intensive English programs in institutions of higher education are nurtured to meet this need. English language textbooks, as the main source of language input for English-as-a-foreign-language (EFL) learners in China, play a critical role in developing leaners’ communicative competence. Therefore, it is significant to examine to what extent the textbooks currently used by English major students facilitate their acquisition of communicative competence. To evaluate pragmatic teaching in the textbooks, this study focuses on five speech acts and surveys the relevant pragmatic input and pragmatic tasks available for these speech acts in four sets of textbooks that are widely used by English majors in China. Both oral-English textbooks and integrated-skills textbooks have been selected to explore if there are skill-based differences in pragmatic teaching. Content analysis has been employed to scrutinize pragmalinguistic and sociopragmatic input as well as pragmatic tasks provided in the focal textbooks. The quantitative and qualitative results show that the textbooks do not provide sufficient conditions for the development of communicative competence. The analysis of pragmalinguistic input, which focuses on the speech act strategies and modification devices included in the textbooks, reveals that the distribution and presentation of these strategies and devices do not reflect naturally occurring speech. As for sociopragmatic input, the textbooks present inadequate contextual information and give little attention to sociocultural norms for speech act performance. Finally, the oral-English textbooks and integrated-skills textbooks do not differ systematically in their treatment of pragmatics in terms of pragmalinguistic input, sociopragmatic input, and pragmatic tasks. The findings of the present study not only provide useful information for further textbook development but also have important implications for textbook use in the classroom.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".