The Lecture Buddy Project: An Experiment in EAP Listening Comprehension
Bibliographic record
Abstract
This article describes a study of the listening comprehension of first-year, non-native speakers of English (NNSs) in a large North American university. The goal was to find out how the students, all economics majors, were coping with listening to economics lectures and to try an experiment in mentoring by linking them with a "lecture buddy": a native speaker in their course who would meet with them weekly and help them with note taking. The lecture buddies kept journals of their meetings, made copies of their lecture notes, and wrote a final report on their experience. In addition, the author interviewed the informants at the end of each semester, and these interviews were transcribed. The study confirms that these students were having substantial difficulty with their lectures, were taking poor notes, and were doing poorly in the courses as a result. The mentoring project was judged to be helpful to the informants, and the help that the lecture buddies gave went far beyond working on note taking. The article ends with a list of recommendations about what the university and the professors could do to make it easier of the NNS students and what the students themselves could do.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".