Bibliographic record
Abstract
A mathematics education has moved to the forefront of education requiring direct and specific action to improve student mathematical accuracy and understanding. An arts based approach to mathematics provides an invaluable addition to the mathematics classroom as it addresses the multiple ways of understanding and expressing knowledge. Recent literature appears to emphasize the positive impacts of arts integration in the classroom. Mason et al. (2005) note, based on a study of the impact of arts education on social, cognitive, and academic skills, “the more math teachers integrate arts into mathematic lessons, the more students gain on their math tests” (p.4). As Kosky and Curits (2008), Gullat (2008), and Werner (2001) suggest, there is a positive relationship between arts integration and academic achievement and participation. That is, students who are taught through arts integration tend to be more actively involved in the process of learning and tend to score higher on various levels of assessment. This research used a best practices approach and semi-structured interviews to delve into the practices of four educators in various locations across Ontario. The findings of this research were grouped into four central themes: 1) Arts are Everywhere; 2) The Teacher Approach to Arts Integration Matters; 3) Arts Integration makes for Student Centered Learning; and 4) There is an Increased Opportunity for Learning Mathematics through the Arts. It is the hope of this research to show new and experienced teachers the perceived benefits of an arts based mathematics program on student achievement and engagement in math. Additionally, this research strives to provide educators with the tools and an introductory framework for implementing an arts based approach to math in their classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".