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Record W2559141242 · doi:10.1109/fie.2016.7757741

Exploring Electrical Engineering through movement: Going with the Flow and Programming Puzzles

2016· article· en· W2559141242 on OpenAlexaff
Emily Marasco, Stephanie Hladik, Laleh Behjat, William Rosehart

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMovement (music)Electron flowComputer scienceEngineering educationWork (physics)DisciplineMathematics educationElectrical engineeringEngineering managementEngineeringMechanical engineeringSociologyElectronPsychology

Abstract

fetched live from OpenAlex

The Exploring Electrical Engineering program will electrify your understanding of engineering! Developed as part of a larger K-6 engineering education research initiative, this workshop paper details two activities for exploring electrical and computer engineering concepts for grade 3-5 students through the use of cross-disciplinary concepts in physical education and movement. Activity 1: Going with the Flow uses human electrons and circuit components to demonstrate electron behaviour in parallel and series circuits. Activity 2: Programming Puzzles introduces code design through life-size maze creation and completion. These creative activities have been tested as part of on-going research work in several classrooms, with over 350 elementary school students participating, and have resulted in an increased interest in electrical engineering as a future career.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.034
GPT teacher head0.212
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2016
Admission routes1
Has abstractyes

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