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Enregistrement W2756338335 · doi:10.18260/p.25511

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

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

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueEngineering Education and Pedagogy
Établissements canadiensMcGill UniversityYork University
Organismes subventionnairesnon disponible
Mots-clésEngineering educationBrainstormingMathematics educationEngineering design processNext Generation Science StandardsDisadvantagedComputer scienceLiving labProcess (computing)Engineering managementEngineeringScience educationPsychologyMechanical engineeringArtificial intelligence

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,626
Score d'incertitude au seuil0,512

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,308
Écart entre enseignants0,271 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2016
Routes d'admission1
Résumé présentoui

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