Beginning to Write With Word Processing: Integrating Writing Process and Technology in a Primary Classroom
Bibliographic record
Abstract
Writing behaviors of grade 1 children were explored as they used word processors to support their writing. Information was gathered in the form of field notes and audiotape transcripts from classroom observation sessions, along with informal interviews with students and teachers. Themes which emerged from the data analysis suggested a combination of influences at work in the classroom environment. These included (a) changes in the classroom culture with regard to tolerance of student talk during writing sessions; (b) the length and characteristics of student–teacher interactions; and (c) teacher instructional practices about writing with and without a word processor. Different aspects of the physical environment where the students wrote—classroom or computer lab—were found to have the potential to influence students' writing behavior and possibly the written work produced. Student writing behavior using word processors and pencil‐and‐paper revealed differences in reading and rereading of the work in progress. In view of these findings, considerations for classroom practice, including (a) developing realistic expectations, (b) adjusting instruction, (c) scheduling feedback, (d) adjusting minilesson timing, and (e) teaching students collaborative work skills are shared.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".