MétaCan
Menu
Back to cohort
Record W2800970763 · doi:10.15695/jso.v1i2.4550

Improving Access to Hands-On STEM Education using a Mobile Laboratory

2018· article· en· W2800970763 on OpenAlexaff
William H. Roden, Rebecca A. Howsmon, Rebecca A. Carter, Mark Ruffo, Amanda L. Jones

Bibliographic record

VenueThe Journal of STEM Outreach · 2018
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsOutreachPreparednessScience educationCurriculumResource (disambiguation)Medical educationPsychologyPedagogyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Schools often have limited resources to devote to science education, which can impact student interest in and preparedness for careers in STEM. Seattle Children’s Research Institute created the Science Adventure Lab, a mo¬bile laboratory program, to support and enrich science education at low-resource schools and stimulate interest in science and pursuing a career in STEM. The mobile laboratory provides students with the unique opportunity to fully immerse themselves in authentic, hands-on science learning with scientists. This limits the burden on school resources and reduces disruptions to daily schedules since students do not leave their schools. These positive science experiences at an early age allow students to learn important science concepts and have the potential to significantly impact students’ interest in pursuing STEM careers. In this report we describe our mobile laboratory, operating model, and curriculum, as well as the positive impacts, strengths, and challenges of the approach as a resource for other groups who may wish to use a similar strategy for STEM education outreach.

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 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.269
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of STEM OutreachSame topicBiomedical and Engineering EducationFrench-language works237,207