Using Music Videos to Inspire Engineering
Bibliographic record
Abstract
The OK Go Sandbox project is a free resource website for K-12 educators.Launched in Spring 2018, the site currently contains 15+ videos and educator guides on topics ranging from engineering concepts (such as simple machines, sensors, and design process) to teamwork and artistic exploration.The content is created in a collaboration between the band OK Go and the Playful Learning Lab at the University of St. Thomas, with K-12 educators involved throughout the planning and implementation phases.Since its launch, educators have used the materials in a variety of ways, some of which the development team had not previously considered.This paper will particularly focus on the implementation and evaluation of the engineering content on the site.We will present and discuss results from (1) educator feedback surveys, (2) website analytics, and (3) educator focus groups.We will also reflect on the challenges and opportunities in promoting new web-based educator materials.Our team has implemented a number of strategies to reach teachers, including social media, conference attendance, and email newsletters.Now that the materials have been available for over 18 months, we are able to share lessons learned from this project and identify the areas that are being explored for further investigation and refinement.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".