“All These Nouns Together Just Don’t Make Sense!”: An Investigation of EAP Students’ Challenges with Complex Noun Phrases in First-Year College-Level Textbooks
Bibliographic record
Abstract
Complex noun phrases (CNP) are a major vehicle of academic written discourse (Halliday, 1988; 2004). However, in spite of the view that they pose significant challenges to English language learners, they are often overlooked in preparatory English for Academic Purposes (EAP) programs. This mixed methods study aims to investigate to what extent CNP present syntactic parsing challenges for upper-level college EAP students, and whether there is a perceived need for direct instruction in CNP in EAP programs. A special CNP proficiency test was administered to 70 upper-level Ontario college EAP students and a native speaker comparator group, and the results were compared with those obtained from interviews with seven of the test-takers. The results obtained from the statistical analyses and the interviews indicate that CNP are challenging to parse for upper-level EAP students and that direct instruction in CNP may be beneficial for improving their reading comprehension. Some teaching implications of the findings are also addressed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".