The write way to spell: printing vs. typing effects on orthographic learning
Bibliographic record
Abstract
Prior research has shown superior orthographic learning resulting from spelling practice relative to repeated reading. One mechanism proposed to underlie this advantage of spelling in establishing detailed orthographic representations in memory is the motoric component of the manual movements evoked in printing or writing. This study investigated this contention directly by testing the effects of typing vs. printing on the orthographic learning achieved through spelling practice, and further evaluated whether practice modality interacts with pre-existing individual characteristics. Forty students in grade 2 (mean age 7 years 5 months) were introduced to 10 novel non-words. Some of the students practiced spelling the items by printing, while the others practiced spelling them on a keyboard. Participants were tested for recognition and spelling of these items 1 and 7 days later. Results revealed high rates of orthographic learning with no main effects of practice modality, testing time, or post-test modality. Hierarchical regression analyses revealed an interaction between typing proficiency and practice modality, such that pre-existing keyboarding skills constrained or facilitated learning within the typing-practice group. A similar interaction was not found between printing skills and learning within the printing group. Results are discussed with reference to both prominent reading theory and educational applications.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".