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Enregistrement W2623422202 · doi:10.18260/1-2--2713

Attracting And Retaining Females In Engineering Programs: Using An Stse Approach

2020· article· en· W2623422202 sur OpenAlexaffabout
Lisa Romkey

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCareer Development and Diversity
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMentorshipSocializationContext (archaeology)CurriculumEngineering educationEngineering ethicsPsychologySociologyPedagogyEngineeringSocial psychologyMedical educationMechanical engineering

Résumé

récupéré en direct d'OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Introduction There are great strides to be made in the recruitment of women to engineering programs and careers. While women typically make up more than 50% of individuals studying life science at most universities and colleges across North America, we see far fewer women in engineering programs than we do males. There is a considerable body of research that suggests ways to improve this, including mentorship programs, a change in the nature of the engineering workplace to accommodate family needs, and creating a more collaborative and less competitive atmosphere in both the academic and industry sides of engineering. Much of the literature on gender studies in science, technology and engineering suggests females enjoy and connect with these fields when they are placed within a human, social or environmental context. This paper demonstrates the why and how of this relationship, drawing ideas from gender roles and gender socialization. This paper looks at how moral development may impact a woman’s choice to pursue a career in the physical sciences, technology, engineering or math. In particular, the paper draws from Gilligan’s theories on females and the care-orientation of moral development, and how her theories demonstrate a need for a STSE (Science, Technology, Society and the Environment) orientation in high school, college and university curriculum. The extensive literature review in this paper is supplemented by qualitative data from 10 semi-structured interviews with female engineering students and recent female engineering graduates from a large engineering school in Canada. The subjects were interviewed individually, and came from a diverse set of academic and cultural backgrounds, engineering disciplines, interests and aspirations. The interviews were conducted in-person or via telephone, and were 30-45 minutes in duration. The interviews were structured around the following list of questions, however, the individuals interviewed were encouraged to share any thoughts on their experience, and some themes developed, and were encouraged, on an individual basis. • Why did you decide to pursue engineering? Do you feel that females have different reasons than males for pursuing engineering? • What were your first experiences with science and engineering as a youth? Which sciences were you most exposed to? • What were your most positive experiences in science and engineering prior to starting university? • Did you have any hesitation about pursuing engineering as a female? • What have been your most positive experiences, academically, as an engineering student? • What do you plan to do with your engineering degree? Do you think females have different goals than males? • Female numbers in engineering remain relatively low, and have recently been on the decline in Canada. Why do you think this is the case? How can we attract more women to the field of engineering? • Do you think there are stereotypes about engineering, or about women, that detract women from pursuing engineering? • Do you think the experience as a student is different for males and females? • If you could change something about your education as an engineer, what would it be?

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,065

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,024
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0040,003
Études des sciences et des technologies0,0060,001
Communication savante0,0070,005
Science ouverte0,0020,006
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0190,003

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 Opus0,260
Tête enseignante GPT0,312
Écart entre enseignants0,052 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
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

Citations8
Publié2020
Routes d'admission2
Résumé présentoui

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