Towards a writing analytics framework for adult english language learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Improving the written literacy of newcomers to English-speaking countries can lead to better education, employment, or social integration opportunities. However, this remains a challenge in traditional classrooms where providing frequent, timely, and personalized feedback is not always possible. Analytics can scaffold the writing development of English Language Learners (ELLs) by providing such feedback. To design these analytics, we conducted a field study analyzing essay samples from immigrant adult ELLs (a group often overlooked in writing analytics research) and identifying their epistemic beliefs and learning motivations. We identified common themes across individual learner differences and patterns of errors in the writing samples. The study revealed strong associations between epistemic writing beliefs and learning strategies. The results are used to develop guidelines for designing writing analytics for adult ELLs, and to propose ideas for analytics that scaffold writing development for this group.
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.
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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it