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Record W2119245592 · doi:10.24908/pceea.v0i0.5868

Designing Age Appropriate Engineering Outreach Activities

2015· article· en· W2119245592 on OpenAlexaffvenue
Martin Scherer, Mary A. Wells

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOutreachPsychologyCognitionSummer campSet (abstract data type)Medical educationMathematics educationComputer scienceEngineeringPublic relationsPolitical scienceMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

For over twenty years, the University of Waterloo’s Faculty of Engineering has been running outreach programming directed at elementary youth (ages 6 to 14) through its Engineering Science Quest (ESQ) summer camp program. All the activities are designed to be hands-on with the primary goal to increase participant’s interest in Science, Technology, Engineering and Math (STEM). The camp develops themes, such as ‘Outer Space’ to help motivate activity development and provide practical examples to participants.In 2010, the directors of ESQ modified the approach to develop camp outreach activities to ensure they were hands-on engaging activities related to science and engineering but also paid consideration to ensuring a secondary set of goals were met that considered the cognitive development of the children in the camp. The result was the development of hands-on outreach activities that engaged the participants in multiple ways.The motivation behind the development of outreach activities with these secondary objectives in mind were based on observation of past successes and working knowledge of the target audience.It is concluded that in order to make truly engaging and effective programming for elementary aged youth; activity developers should develop hands-on activities that incorporate both the interests of youth and their appropriate cognitive development stage. Using these methods in activity development will lead to an increase in success and a stronger impact of the programming.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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.

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

Citations1
Published2015
Admission routes2
Has abstractyes

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