The Role of Information Management in the Assessment of Grammar in L2 Academic Writing
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
Information management of discourse – the ability of a writer to use linguistic forms to organize and present information in a written text – is a key component of second language (L2) ability models in the language assessment literature (e.g., Canale & Swain, 1980; Weigle, 2002), but Purpura’s (2004) language ability model developed specifically for assessment purposes is the only one that considers it to be part of the ability to use grammar accurately and meaningfully when producing a text in an L2. The current study investigated whether L2 academic writing teachers consider information management of discourse as an assessment criterion when assessing grammar in L2 academic texts. Fourteen students in an academic English as a second language writing course at an English-medium university in Canada and their teacher participated in this case study. Students’ essay exam scripts were collected, and the Theme-Rheme progression (TRP) patterns and links (Daneš, 1974) as well as the distribution of new and given information (Halliday & Matthiessen, 2004) in these essays were analyzed. Pearson correlation coefficients between the teacher-assigned grammar grade and the results from the TRP and information distribution analyses were calculated. The findings indicate that information management of discourse indeed forms part of the assessment criteria for grammar in academic writing for the teacher in this study. The implications of this finding for L2 writing pedagogy are discussed.
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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 | 0.000 |
| 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 | 0.000 |
| 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 it