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Enregistrement W3109270888 · doi:10.18260/1-2--32961

Increasing the Interest of Elementary School Girls in STEM Fields Through Outreach Activities

2020· article· en· W3109270888 sur OpenAlexaffabout
Jennifer Bastiaan, Roger Bastiaan

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCareer Development and Diversity
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésOutreachWorkforceMathematics educationEngineering educationEvent (particle physics)PsychologyMedical educationEngineeringPolitical scienceMedicinePhysicsMechanical engineering

Résumé

récupéré en direct d'OpenAlex

Despite the known value of a diverse Science, Technology, Engineering and Mathematics (STEM) workforce, women and minorities continue to be under-represented in these fields. Engineering undergraduate degrees, in particular, are awarded to women engineering students in the United States and Canada at a lower rate compared to their male counterparts. For the past 20 years, less than 20% of engineering degrees have been awarded to women students, and this stubborn trend is not changing much. The outcome is worse for black and Hispanic students, who usually comprise less than 10% of engineering graduates. Research has shown that low self-confidence in learning math and science subjects starts at a young age in girls and minority students, often in the early years of elementary school, and this ultimately leads to low interest and enrolment in STEM undergraduate programs. In an attempt to combat negative stereotypes about the capabilities of girls and minorities in STEM studies, which undermine the confidence of these groups, the Society of Women Engineers (SWE) has instituted the Girls’ Engineering Exploration (GEE) day. This is an annual STEM outreach event for girls in the Detroit Public School (DPS) system, which is 95% black and Hispanic. GEE is an all-day event for 4th to 6th grade female DPS students. Groups of girls participate in the event with volunteer mentors who are female engineers working in local industry, thus providing the girls with role models. The groups of girls and their mentors cycle through a series of STEM activities that are meant to be engaging, and to increase their interest in STEM careers. In this work, two GEE activities recently created and presented are described in detail. The first activity is a traditional engineering exercise involving physical creation and observation of electrical circuits. The second activity is a novel exercise focused on the new discipline of autonomous vehicle design. The girls experiment with “doodle track cars”, which are inexpensive toy cars that stand in for self-driving vehicles. The toy cars are equipped with optical sensors which enable them to follow hand-drawn lines that represent the roadway. This activity allows the girls to investigate the limitations of real sensors. All of the materials for both activities are provided as educational resources, including science sheets and worksheets, such that pre-college educators can take advantage of these activities in their own classrooms and outreach events with little to no modification. Detailed information about the design and deployment of these activities is reported, including cost of materials and opportunity cost, in terms of time invested in preparing the activities for students. Furthermore, the results of student surveys from GEE, in the form of questionnaires for each activity, are analyzed and presented. The conclusion is that modern topics such as autonomous vehicles are well worth the activity development effort, as students are more engaged in these activities than in derivative exercises such as the circuits activity, which they may have been exposed to previously.

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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,757
Score d'incertitude au seuil0,998

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)0,0010,000

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 Opus0,100
Tête enseignante GPT0,297
Écart entre enseignants0,197 · 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'étudeObservationnel
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

Citations1
Publié2020
Routes d'admission2
Résumé présentoui

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