Test Takers' Writing Activities During the<i><scp>TOEFL iBT</scp><sup>®</sup></i>Writing Tasks: A Stimulated Recall Study
Bibliographic record
Abstract
This study aimed to describe the writing activities that test takers engage in when responding to the writing tasks in theTOEFL iBT®test and to examine the effects of task type and test‐taker English language proficiency (ELP) and keyboarding skills on the frequency and distribution of these activities. Each of 22 test takers with different levels of ELP (low vs. high) and keyboarding skills (low vs. high) responded to 2TOEFL iBTwriting tasks (independent and integrated) on the computer. Each participant then provided stimulated recalls about the writing activities they used when performing each writing task. Stimulated recalls were coded and the results were compared across tasks and test‐taker groups. The findings indicated that the participants engaged in various construct‐relevant activities, such as interacting with the writing task and resources, planning, generating, evaluating, and revising. Additionally, test takers' writing activities varied significantly across tasks and to a lesser extent across test‐taker groups. Participants' writing activities varied most across writing tasks and, to a lesser extent, across English proficiency groups. Low keyboarding skills seem to have affected mainly activities on the independent writing task. To better understand the role of keyboarding skills in performance on theTOEFL iBTwriting tasks and to address the test's extrapolation inference, future studies need to compare the writing performance of test takers with different levels of second language (L2) proficiency and keyboarding skills in test and nontest settings.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".