Effects of Transcription Mode on Word-Level Features of Compositional Quality among French Immersion Elementary Students
Bibliographic record
Abstract
Transcription is an important component of the writing process that affects the quality of children’s compositions. However, little is known about how transcription mode influences productivity or spelling accuracy, two word-level markers of compositional quality, among children learning to write in an additional language. To address this issue, we compared the effects of handwriting and keyboarding on text length and spelling in the compositions of L2 French learners. Grade 2 to 4 students (n = 48) in French Immersion were given two writing prompts and asked to produce one text on paper and one using a keyboard. The prompts were counterbalanced across the two writing conditions. The total number of words, total number of words spelled correctly, and proportion of correctly spelled words were calculated. A series of repeated measures ANOVAs revealed an advantage in both the average number of correctly spelled words and the proportion of correctly spelled words in the students’ compositions favouring the keyboarding condition. Conversely, the total number of words across conditions was not significantly different. Our findings suggest that keyboarding may offer an advantage over handwriting with respect to spelling accuracy in the context of L2 composition in the elementary years.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".