A Case Study of ESP Practice: Towards Better Tutoring of College Student Graduation Thesis Abstract and Introduction Writing
Bibliographic record
Abstract
This research is a case study approach towards the better tutoring of college student graduation thesis abstract and introduction writing as English for Specific Purposes practice. Based on the situation in Shanghai University of Electric Power (SUEP), this study starts with the exploration of what was claimed by Vijay K Bhatia as something of which “even expert members of the academic community sometimes fail to make a proper distinction” (Bhatia, 1993, p.76), that is, the differences between two genres of academic writing, namely, abstract and introduction. Thirty-five pairs of SUEP student abstracts and introductions, chosen at random, were analyzed, ten SUEP thesis tutors were given a questionnaire about their way of tutoring in thesis writing, and ten currently-used reference books for essay writing were reviewed, in order to see whether any problems actually exist in real thesis writing and tutoring practice. In the research, three main defects were found indeed in the student abstracts and introductions: (a) failure to observe proper moves normally adopted to realize the communicative functions of the abstract and introduction, (b) content misplacement and, (c) irrelevant ideas in the two parts of the essay. These problems seem to have resulted from the students’ unawareness or confusion of the two different genres of writing. Thesis tutors and reference materials checked in this study, however, were found not to pay enough attention to the distinction either, thus echoing Bhatia’s statement mentioned above. Then, after a careful discussion of differences between abstract and introduction from a comparative perspective, this study concludes that the following factors might be decisive to improve the situation: (a) specific move-based requirements for abstract and introduction writing in students’ essay writing, (b) thesis tutors’ awareness of the necessity to help students notice and realize these differences as a demonstration of academic writing competence, (c) practical related reference materials for students, (d) reasonable assessment standards to consolidate the necessity of the distinction, (e) lectures to teach such important points as move-structure, and commonly used phraseology in each move, and (f) further attention to similar issues such as citation, referencing and conclusion to see if they are also dealt with according to internationally accepted academic practice in students’ essay writing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".