Tracing writing progression in English for academic purposes: A data-driven possibility in the post-COVID era in Hong Kong
Bibliographic record
Abstract
It is rare to use “big data” in writing progression studies in the field of second language acquisition around the globe. The difficulty of recruiting participants for longitudinal studies often results in sample sizes that are too small for quantitative analysis. Due to the global pandemic, students began to face more academic and emotional challenges, and it became more important to track the progression of their writing across courses. This study utilizes big data in a study of over 4,500 students who took a basic English for Academic Purposes (EAP) course followed by an advanced one at a university in Hong Kong. The findings suggest that analytics studies can provide a range of insights into course design and strategic planning, including how students’ language use and citation skills improve. They can also allow researchers to study the progression of students based on the level of achievement and the time elapsed between the two EAP courses. Further, studies using mega-sized datasets will be more generalizable than previous studies with smaller sample sizes. These results indicate that data-driven analytics can be a helpful approach to writing progression studies, especially in the post-COVID era.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".