A Retrospective Analysis of the Impact of SpaceMath@NASA on Student Performance in Math and Science
Bibliographic record
Abstract
Real world, mathematics-based educational activities provide context for learning and break down barriers to learning in mathematics and science. SpaceMath@NASA (hereafter SpaceMath) provides teachers with real-world math activities in a space context in support of standards, by using current NASA discoveries as a starting point for motivating students to develop and use mathematics skills. The reach and efficacy of SpaceMath in supporting NASA’s STEM mission was examined through an analysis of the resources and website data, a survey of a subset of listserv members, data from workshop attendees - new users of SpaceMath, and a comparison group study. SpaceMath has been used by millions of educators who consistently report that SpaceMath aligns with what they teach, that they can immediately apply what they have learned in workshops, and are able to use it in their classes. Educators report that students enjoy the application problems and topics, are productively engaged, and ask questions that demonstrate curiosity and interest. Use of SpaceMath to teach science concepts and apply math skills provides a context that enhances student understanding.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".