Democratizing Children's Computation: Learning Computational Science as\n Aesthetic Experience
Bibliographic record
Abstract
In this paper, we argue that a democratic approach to children's computing\neducation in a science class must focus on the aesthetics of children's\nexperience. In Democracy and Education, Dewey links "democracy" with a\ndistinctive understanding of "experience". For Dewey, the value of educational\nexperiences lies in "the unity or integrity of experience" (DE, 248). In Art as\nExperience, Dewey presents aesthetic experience as the fundamental form of\nhuman experience that undergirds all other forms of experiences, and can also\nbring together multiple forms of experiences, locating this form of experience\nin the work of artists. Particularly relevant to our current concern\n(computational literacy), Dewey calls the process through which a person\ntransforms a material into an expressive medium an aesthetic experience (AE,\n68-69). We argue here that the kind of experience that is appropriate for a\ndemocratic education in the context of children's computational science is\nessentially aesthetic in nature. Given that aesthetics has received relatively\nlittle attention in STEM education research, our purpose here is to highlight\nthe power of Deweyan aesthetic experience in making computational thinking\navailable to and attractive to all children, including those who are\ndisinterested in computing, and especially those who are likely to be\ndiscounted by virtue of location, gender or race.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".