Art and music as a teaching aid for STEM subjects
Bibliographic record
Abstract
Science, technology, engineering, and math (STEM) subjects have historically struggled to be inclusive and accessible to students from diverse backgrounds. Furthermore, STEM subjects have often been rigid in their teaching structure, creating barriers to education for students with more complex learning needs. Recently, there has been an increased need for compassionate pedagogy and adaptive education practices to provide multi-modal learning experiences. Our STEM outreach course, Think Like A Scientist, has been running in a number of English prisons since 2019, and started in Canada in 2023. Our students in prison often have diverse learning needs and the classroom presents numerous barriers (sensory, communication, processing, and regulation). This particularly impacts those considered with forms of neurodivergence (e.g., autism, ADHD, OCD, dyslexia, etc). In our teaching in prison, we have been conscious of creating different educational access points that is not focussed on rote learning and reading text (which some students struggle with). In particular, we have been using creative practices, including art, poetry and music, as a teaching aid for geoscience subjects such as climate change.In this submission, we outline how we have created a collaborative space between artist and student to co-create unique art and music that stimulates learning and engagement. Although our outreach programme is tailored to the restrictive prison environment, the application of its core principles to education are fundamental EDI practices that could be beneficial to a wide audience. Our work aims to increase educational engagement for students under the neurodivergent umbrella, fostering a classroom environment that is inclusive and accessible to all.  
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".