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Record W2756338335 · doi:10.18260/p.25511

HYPOTHEkids Maker Lab: A Summer Program in Engineering Design for High School Students

2016· article· en· W2756338335 on OpenAlexaff
Aaron M. Kyle, Rachel Sattler, Hanzhi Zhao, Christine Kovich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsMcGill UniversityYork University
Fundersnot available
KeywordsEngineering educationBrainstormingMathematics educationEngineering design processNext Generation Science StandardsDisadvantagedComputer scienceLiving labProcess (computing)Engineering managementEngineeringScience educationPsychologyMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The continued emergence of STEM careers and emphasis on engineering in the Next Generation Science Standard (NGSS) spurs the need for early (P-12) engineering education. There are persistent deficits in engineering education, for underrepresented minority groups and in lower-resource schools. To address these deficits, we have created the HYPOTHEkids (Hk) Maker Lab, a six week summer program (120+ hours) in which high school students, specifically those from underrepresented minority groups and economically disadvantaged New York City high schools, are introduced to the biomedical engineering design process. Biomedical engineering design is an appealing mode of instruction because it entails the practical application of science, mathematics and technology knowledge that students have accrued throughout their education, giving them a real-world appreciation for these skills and fostering continued interest in STEM. The Hk Maker Lab, which is free for all participants, engages students who would not normally have an engineering-focused pre-college experience. During the first three weeks of the program, students are taught design through a series of interactive workshops. Students learn needs identification; customer discovery and design inputs; brainstorming to devise solutions; and proof of concept testing. They are also introduced to the entrepreneurial aspects of device innovation, including the formation of business models. The workshops are complemented by daily, hands-on laboratory sessions that introduce biomedical concepts and basic engineering skills, including instrumentation design and testing, programming (MATLAB and Arduino), and fabrication techniques (laser cutting, 3-D printing). The second half of the program is devoted to the participants creating testable prototypes that satisfy the needs uncovered during the design workshops. The Hk Maker Lab culminates in students presenting their innovations at a final pitch event, where the projects are evaluated by a panel of judges from academia, industry, and entrepreneurial sectors. The Hk Maker Lab has been conducted in the summers of 2014 and 2015, with 24 participants in each group. We have achieved significant underrepresented minority participation: 52% of the students have been Hispanic or African American, 50% have been female. Hk Maker Lab participants have successfully developed prototypes ranging from a re-chargeable LED array hospital light to provide illumination in low resource hospitals, to a fall detection and alarm device for the elderly, to a device to sterilize used hypodermic needles to prevent secondary infections from needle-sticks. The program has had a positive impact on students’ interest in engineering. More than 90% of our students attribute their interest in pursuing engineering in college to their participation in the Hk Maker Lab. Program alumni have gone on to internships in biotechnology and/or are currently pursuing engineering undergraduate majors. We propose that biomedical engineering design provides critical pre-college engineering education for groups underrepresented in STEM. This paper provides a framework for the creation of an engineering design-focused program. Through the Hk Maker Lab, we have devised a biodesign curriculum that can be readily taught to high school students and best practices for implementing this type of program.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.626
Threshold uncertainty score0.512

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 head0.037
GPT teacher head0.308
Teacher spread0.271 · 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 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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