Bridging disciplinary aesthetics: when mathematics meets art through educational technology
Bibliographic record
Abstract
In this article, we discuss the way students’ aesthetic experiences can shape the learning of mathematics at higher school levels. We designed a learning environment based on three main design principles: (1) Mathematics as Artistic, where mathematics is used for open artistic creation; (2) Aesthetically rich mathematical experiences, that enable students to appreciate mathematical beauty and aesthetic experiences of wondering, imagining, conjecturing, testing, discovering, making connections, problem posing and solving; (3) Constructionism, where mathematical sense making is interwoven with constructing a personally meaningful digital artefact. Two students of the 11th grade participated in a case study, where they used expressive digital resources for representing, manipulating and exploring periodic functions in order to create an animated artefact based on a piece of music. The collected data fed the formation of a theoretical model for analysing students’ aesthetically driven mathematical meaning making, in an attempt to structure existing theoretical concepts around mathematical aesthetics in education. A part of the analysis of students’ aesthetic experiences based on this model is presented and further reflectively discussed with respect to the prospect of an aesthetically oriented curriculum reform.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".