Examining the impact of L2 proficiency and keyboarding skills on scores on TOEFL-iBT writing tasks
Bibliographic record
Abstract
A major concern with computer-based (CB) tests of second-language (L2) writing is that performance on such tests may be influenced by test-taker keyboarding skills. Poor keyboarding skills may force test-takers to focus their attention and cognitive resources on motor activities (i.e., keyboarding) and, consequently, other processes and aspects of writing (e.g., planning, revising) might be left unattended to, which can lead to poor text quality and lower test scores. Such effects might be more pronounced for L2 test-takers. This study investigated the impact of keyboarding skills on test-takers’ scores in the context of the TOEFL-iBT Writing Section. Each of 97 test-takers, with different levels of English language proficiency (low vs. high) and keyboarding skills (low vs. high), responded to two TOEFL-iBT writing tasks (independent and integrated) on the computer. Test scores were statistically compared across tasks and test-taker groups. The findings indicated that overall English language proficiency and writing ability in English contributed substantially to variance in task scores, while keyboarding skill had a significant, but weak, effect on task scores. Additionally, keyboarding skills effects depended on task type. While these findings support the claim that performance on TOEFL-iBT writing tasks depends mainly on test-taker English language proficiency, they also raise important questions about the relationships between keyboarding skills, L2 writing ability, and performance on CB L2 writing tests, as well as factors affecting these relationships.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".