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Accessible and Flexible Delivery Methods in Experiential Space Education

2022· article· en· W4311888844 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Aerospace Research and Development · 2022
Typearticle
Languageen
FieldEngineering
TopicSpace Exploration and Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsExperiential learningSpace (punctuation)Computer scienceExperiential educationKnowledge managementHuman–computer interactionPsychologyMathematics education

Abstract

fetched live from OpenAlex

To date, AlbertaSat's educational outreach team has shared our passion for space with over 6500 students across Western Canada through curriculumbased lessons and hands-on activities. The COVID-19 pandemic has created a need to adapt the way educational activities are conducted and has shown just how important accessibility and flexibility is when it comes to education. We accepted the challenge to adapt our lessons with regards to delivery methods. Our teaching philosophy underwent a thorough process of iteration, improvement, and development for in-person, hybrid, and remote learning. In the process of revitalizing our lessons, we also focused on relating curriculum outcomes to the real world. We introduce students from kindergarten to grade nine to a selection of different space science roles. Our sessions range from satellite design to software and electronics development to Computer Aided Design (CAD). For high-school classrooms, we work with teachers to create individualized lessons for their students' skills and interests. Our efforts bring the cutting edge into the classroom and show students that careers in aerospace are within their reach.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.064
GPT teacher head0.423
Teacher spread0.359 · 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