Dancing brains: dance as a key motivator for success in mathematics
Bibliographic record
Abstract
A growing body of research supports the notion that dance enhances cognitive function as well as providing an enjoyable means of learning, as evidenced by recent news items and experiments such as that of Professor Michael Duncan of Coventry University as shown in the recent BBC documentary ‘The Truth About Getting Fit’ (BBC 50:43-57:00) where dance was declared “unusually beneficial” (Michael Mosley, 50:47) for the brain. Lynnette Overby, Beth Post and Diane Newman espouse the “bodies-on” nature of interdisciplinary dance stating that dance is: Uniquely suited to support conceptual learning because the dance vocabulary is expressed in terms of the body, space, time, and force – concepts also fundamental to understanding the universe (2005, Preface xi). Other scholars such as Anne Watson, Anne Green-Gilbert (BrainDance) and Eric Jensen, and on-going programmes such as Learning Through the Arts and Project Zero support the notion that dance is beneficial for the mind and useful as a means of interdisciplinary learning. In addition, neuroscience research shows that 85% of learners are predominantly kinesthetic learners (Jensen, 2010) and the President’s Committee on the Arts and the Humanities agrees that there are: Documented significant links between arts integration models and academic and social outcomes for students, efficacy for teachers, and school-wide improvements in culture and climate (PCAH 2011 in Wheeler and Bogard 2013, p.4). In my action research project carried out in Primary Schools in Canada, using a quasi-experimental approach and pre-/post data, it was clear that the increase in motivation to learn, along with increase in attainment was evident with students also enjoying both subjects more than they anticipated or experienced prior. In this paper, therefore, I will explore the notion of an equal interdisciplinary partnership of dance and mathematics that increases motivation and enhances learning in both subjects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".