The Role of EAP Genre-Focused Instruction in Preparing Novice Research Students for Thesis Writing: A Case Study
Bibliographic record
Abstract
Although writing master’s theses are believed to be a major challenge for many L2 research students, there has been no extensive discussion about to what extent students are prepared for such advanced academic writing through learning in English for academic purposes (EAP) classes. This study investigated a group of novice research students learning to write master’s theses in an EAP course at a Chinese university and explored their progress in developing genre knowledge. Data were drawn from interviews, participants’ learning diaries, and their written texts. It was found that most learners had developed the macro-level formal genre knowledge, including the overall structure and content of thesis writing, and raised the declarative meta-cognitive genre awareness, but they had not yet grasped the tacit aspects of rhetorical knowledge, the micro-level formal knowledge, and the complicacy of process knowledge, including the abstract thinking processes, intertextuality, and the interpersonal meaning of academic texts, as well as the correspondent lexicogrammatical features. The nascent status of the students’ genre knowledge developed in the EAP class, and the role of EAP genre-focused instruction in preparing novice research students for their future thesis writing, are further discussed. It is suggested that thesis-focused EAP writing courses take advantage of explicit instruction to inform students about the meta-generic specifications of thesis writing and emphasize the multiple dimensions of genre knowledge development.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".