Understanding text-based studies of linguistic development through goals for academic writing
Bibliographic record
Abstract
Numerous studies of written language development have shown minimal improvement over the course of an instructional period, yet these solely text-based studies offer little explanation for the lack of changes in writers’ language because little is reported about the classroom and the participants. The purpose of this study is to use goal theory to better understand students’ instructional contexts and their writing behaviors as they relate to textual features. We focused on six students in two English for academic purposes (EAP) writing classes, in which we conducted observations and teacher interviews. Students filled out an open-ended survey, wrote two essays using keystroke logging software, and participated in stimulated recall sessions. We first evaluated how instructional goals and practices aligned with students’ goals and writing behaviors. We first found that the instructors’ and students’ language-related goals did not completely match. Students had the goal of improving their language, but teachers focused on more global writing issues. In addition, teachers had a goal that students become independent editors of their language, but students missed opportunities to edit. A secondary yet important methodological finding was that the use of timed writing tasks in text-based studies did not allow students to apply explicit grammatical knowledge that the instructors expected the students to have. Pedagogical, research design, and assessments implications are provided.
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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 | 0.011 | 0.046 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".