Connections Between Children’s Motivations Toward Writing and Writing Competence
Bibliographic record
Abstract
This paper explores how young children’s motivation to engage in writing processes aligns with their demonstrated writing competencies. Additionally, it examines connections between children’s self-concept as writers and their writing performance. During group research sessions conducted over the course of four years, three cohorts of 336 children in total, from Kindergarten to Grade 2, completed a prompted narrative writing task and a semi-structured language and writing attitude interview. A research assistant scored the narrative writing samples for quality and connection of ideas, using a six-point holistic scale, while another research assistant recorded children’s interview responses. In general, those children reporting a positive attitude towards writing and a positive self-concept as writers displayed greater competence in writing, as evidenced by higher writing quality scores. This further supports the role that affect plays in motivation and achievement. Interestingly, some children displayed a disconnect between their writing attitude, self-concept and their writing competence, with some children reporting positive attitudes, yet demonstrating low writing competence and others reporting negative attitudes, but demonstrating high writing competence. More in-depth interviews were conducted with three children whose responses showed a disconnect, thereby identifying more nuanced factors in the relationship between attitude and writing competence.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".