Exploring<scp>EAP</scp>instructors' evaluation of classroom‐based integrated essays
Bibliographic record
Abstract
Due to its authenticity as an academic writing task, integrated writing assessment has become widely used for assessing the writing ability of English for academic purposes (EAP) students (Plakans & Gebril, 2017). However, apart from validation studies focusing on a common set of standardized test rubrics (e.g., Chan, Inoue, & Taylor, 2015), little research has explored the construct of integration or how to assess it effectively in EAP contexts. To provide second language (L2) writing researchers and practitioners with an empirically based model of source integration, this study explored EAP instructors' orientations when assessing integrated essays and investigated the relationship between instructor ratings and textual measures of source use. Six experienced EAP instructors first evaluated two sample argumentative essays written by undergraduate English L2 students and provided comments through a stimulated recall interview. Next, they evaluated 48 additional argumentative essays using an analytic rubric that included dimensions of source use and provided comments for each essay. Finally, the essays were analyzed for linguistic and rhetorical features associated with source integration. Triangulation of data sources revealed that the instructors oriented to three aspects of source integration: using source information to support personal claims, incorporating source‐text language in students' own words, and representing source content accurately. Their ratings were correlated with text‐based measures of students' source use. Implications for teaching and assessing integrated writing in EAP settings are discussed.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".