Investigating the Use of Children's Artwork as an Observation Tool in Early Reading Programs.
Bibliographic record
Abstract
This is a report on research conducted over the past six years on the relationship between children's art and reading levels. Findings from one study in Grenada primary schools and three in Ontario suggest that children's picture-making is governed more by mind than hand, and that there may be common cognitive skills, gender and age effects, and traits that affect reading and art. Background, Objectives, and Previous Related Investigations Perhaps the place to begin this paper is in clarifying what it is not. It is not about justi fying the place of art in school programs, nor about integrating the visual and lan guage arts. It is about considering pupils' artworks as manifestations of thoughts and performance styles?as windows for teach ers in primary level reading programs. It reviews selected references pertaining to connections between reading and art and to the content and organization of young children's pictures in addressing the ques tion of whether their artwork can provide useful information for reading instruction. Incentive for the investigation evolved from my own preliminary studies and in terest over decades in debate among art educators about children's artistic devel opment (Lowenfeld & Brittain, 1975), art as communication/language (Cohen & Gainer, 1984; Dorn, 1999), and art as cog nitive activity ( Dorn, 1999; Nelson et al, 1998). The research question was also in spired by Gardner's theory of multiple intelligences (1983), and conjecture that artistic works provide a visible display of many of these, as well as the maker's ac quired knowledge, experience and skill with media, and approach to tasks. What brought these together at the outset was an opportunity to study paintings made by young children in Grenada schools, and to collaborate with their visiting reading in structor. We were aware that her new pu pils were mainly emergent and early read ers who had almost no experience with art materials either at home or school, and we wondered if their first crayoned and painted pictures would provide useful knowledge about both subject areas. A principle hy pothesis for the research that followed was that there would be some similar qualities to and antecedents for their art and lan
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".