What and When Second‐Language Learners Revise When Responding to Timed Writing Tasks on the Computer: The Roles of Task Type, Second Language Proficiency, and Keyboarding Skills
Bibliographic record
Abstract
This study contributes to the literature on second language (L2) learners’ revision behavior by describing what, when, and how often L2 learners revise their texts when responding to timed writing tasks on the computer and by examining the effects of task type, L2 proficiency, and keyboarding skills on what and when L2 learners revise. Each of 54 participants with 2 levels of L2 proficiency (low vs. high) and 2 levels of keyboarding skills (low vs. high) responded to timed independent and integrated writing tasks on the computer. A keystroke logging program recorded each participant's writing activities. Keystroke data were coded in terms of participants’ revision behavior (e.g., orientation, linguistic domain, and temporal location of revisions) and then compared across tasks and learner groups. The findings suggest that the participants tended to revise form more often than content and that L2 proficiency and, to a lesser extent, task type, but not keyboarding skills, affected participants’ revision behaviors during the timed writing tasks. Overall, the participants made more precontextual (that is, at the point of inscription) revisions than contextual revisions (that is, revisions of already written text), made considerably more typography and language revisions than content revisions, revised more frequently at the phrase and word level than at higher levels, and tended to make precontextual revisions more frequently in the first two thirds of the writing process and contextual revisions most frequently in the last third of the writing session. The findings and their implications for practice and research 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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".