Results of a Language Needs-Analysis Survey of Saga University Freshman and Sophomores in Native-Speaker Active English Classes
Bibliographic record
Abstract
In 2006, a new program was established at Saga University with the primary purpose of improving students' English communication ability. The program in its current form has three elements: 1) one-credit Active English (AE) classes for Japanese freshmen (Honjo) and sophomores (Nabeshima); 2) non-credit classes in academic writing (2 levels), academic speaking (2 levels), TOEFL strategies and practice, and TOEIC strategies and practice for all Saga students through the International Student Center; and 3) various English for Academic Purposes courses (2 credits each) specifically for individual departments. The teachers of the classes are all native peakers of English, each with decades of English-teaching experience in various countries of North America, Asia, and Africa. Two of them are from Canada, two from the U.S., and one from Austraila. While the Saga University system labels all the AE classes, regardless of level, as simply「英語 N」, where N=native English-speaking teacher, the English labels are more descriptive, denoting both the style of the classes (active) and students' English skill level at entry: AE 1, AE 2, and AE 3. The survey was given to only the AE students, so all references that follow are to those students, only.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".