Using Prewriting Tasks in L2 Writing Classes: Insights from Three Experiments
Bibliographic record
Abstract
Even though collaborative prewriting tasks are frequently used in second language (L2) writing classes (Fernández Dobao, 2012; Storch, 2005), they have not been as widely researched as other tasks, such as collaborative writing and peer review. This article examines the effectiveness of collaborative prewriting tasks at encouraging English for Academic Purposes (EAP) students to engage in critical reflection while brainstorming the content and organization of written texts. Drawing upon data from three experiments (Neumann & McDonough, 2014a, 2014b), the impact of task design and students’ perceptions about collaboration on their prewriting discussions are explored. Suggestions for instructors with an interest in using collaborative prewriting tasks are provided.Les tâches collaboratives de préparation à la rédaction sont communes dans les cours de rédaction en langue seconde (Fernández Dobao, 2012; Storch, 2005); par contre, elles n’ont pas aussi souvent fait l’objet de recherche que d’autres tâches comme la rédaction collaborative et l’examen par les pairs. Cet article examine dans quelle mesure les tâches collaboratives de préparation à la rédaction encouragent les étudiants en anglais académique à réfléchir de façon critique pendant les séances de remue-méninges sur le contenu et l’organisation de textes écrits. Puisant dans des données découlant de trois expériences (Neumann & McDonough, 2014a, 2014b), nous explorons l’impact qu’ont l’élaboration de la tâche et les perceptions des étudiants quant à la collaboration sur leurs discussions pendant la préparation à la rédaction. En fin d’article, nous présentons des suggestions qui visent les enseignants intéressés à employer des tâches collaboratives de préparation à la rédaction.
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.
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 | 0.000 | 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 | 0.001 | 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) | 0.003 | 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".