MOTIVATION OF SECOND- AND SIXTH-GRADE STUDENTS REGARDING INDIVIDUAL AND COLLABORATIVE WRITING
Bibliographic record
Abstract
Writing motivation decreases in elementary school.This is an issue of major importance considering how it relates to writing skills which are necessary for education as a whole.Teachers must introduce students to the pleasure of learning, and working collaboratively is proposed as one way to sustain students' interest in writing.However, very little is known about the contribution of collaborative writing to writing motivation.This is because few studies have opted for recognized motivation indicators or a control design that would reveal the added value of a collaborative approach at different times in elementary school.The aim of this study is to compare the writing motivation of second-and sixth-grade students depending on whether they write individually or in dyads (repeated measures).After each writing session (individually and in dyads), the students answered a likert-scale questionnaire to assess their motivation on four indicators: self-competence, self-efficacy, interest and value.Preliminary results (t-tests) showed that second-graders are equally motivated to write in both contexts but that boys' motivation reached girls' only in the dyad context.Results for six-graders are expected.They will be presented and discussed to highlight the contribution of collaborative writing to writing motivation, which may vary depending on students' sex and grade level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".