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Record W3089168233 · doi:10.70725/688842bmwbgb

A Retrospective Analysis of the Impact of SpaceMath@NASA on Student Performance in Math and Science

2020· article· en· W3089168233 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computers in Mathematics and Science Teaching · 2020
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsLearning Partnership
FundersOffice of Naval ResearchGoddard Space Flight CenterNational Aeronautics and Space Administration
KeywordsMathematics educationMathematicsComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.277
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it