WORD PROCESSING AND SECOND LANGUAGE WRITING: A LONGITUDINAL CASE STUDY
Bibliographic record
Abstract
The purpose of this study was to determine whether word processing might change a second language (L2) leamer's writing processes and improve the quality of his essays over a relatively long period of time. We worked from the assumption that research comparing word-processing to pen and paper composing tends to show positive results when studies include lengthy terms of data collection and when appropriate instruction and training are provided. We compared the processes and products of L2 composing displayed by a 29-year-old, male Mandarin leamer of English with intermediate proficiency in English while he wrote, over 8 months, 14 compositions grouped into 7 comparable pairs of topics altemating between uses of a lap-top computer and of pen and paper. Al1 keystrokes were recorded electronically in the computer environrnent; visual records of al1 text changes were made for the pen-and paper writing. Think-aloud protocols were recorded in al1 sessions. Analyses indicate advantages for the word-processing medium over the pen-and-paper medium in terms ofi a greater frequency of revisions made at the discourse level and at the syntactical level; higher scores for content on analytic ratings of the completed compositions; and more extensive evaluation ofwritten texts in think-aloud verbal reports.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".