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Enregistrement W2204643176 · doi:10.18260/1-2--21682

Measuring Undergraduate Student Perceptions of the Impact of Project Lead The Way

2020· article· en· W2204643176 sur OpenAlexaboutno aff
Noah Salzman, Eric L. Mann, Matthew Ohland

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueBiomedical and Engineering Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEngineering educationLikert scalePsychologyMathematics educationMedical educationQuarter (Canadian coin)EngineeringEngineering managementMedicineGeography

Résumé

récupéré en direct d'OpenAlex

A survey was distributed to the entire undergraduate student body at a large public university on students’ experiences in Project Lead The Way, a popular middle school and high school technology and engineering program. The survey included demographic questions including academic major, questions on which PLTW classes the students took in high school, and Likert-type ratings of those experiences.Of the responses to the survey (n=575), slightly fewer than half (n=252) indicated that they had participated in PLTW classes in high school. Approximately half of the respondents were majoring in engineering, one quarter in engineering technology, and the rest were distributed among the other colleges of the university. The most popular engineering majors indicated were mechanical engineering, electrical and computer engineering, civil engineering, and aeronautics and astronautics engineering. The most popular engineering technology majors were mechanical engineering technology,electrical and computer engineering technology, and computer graphics technology. 89%of the respondents were Caucasian, and 75% were male.Respondents were generally positive about the program, indicating that they looked forward to the classes, felt that the classes gave them a better appreciation of engineering and technology, and that the classes influenced their choice of major. Differences between the responses of engineering versus technology majors, those majors combined versus all other majors, and male respondents versus female respondents were generally small and not statistically significant.The survey also included an open response portion, where participants were asked if there was anything else they wanted to share about their PLTW experience. Many participants indicated that their experience helped them in choosing a college major and preparing them for college and helped them to learn and think like an engineer. Many participants also described their PLTW experience as “fun,” but although such comments are clearly positive, they do not advance our understanding of PLTW, because students have different ideas about what makes an activity fun. Further, if PLTW were “fun” at the expense of achieving important learning objectives, it would be a disservice. Participants were frustrated by the lack of college credit for their PLTW courses, poor teaching, and feeling like they were better prepared for technology coursework than engineering.The popularity of Project Lead the Way and the resources committed to the program nationally make it urgent that we develop a greater understanding of who this program is reaching and what outcomes result from participation. Further, it will be important to explore the mechanisms by which PLTW achieves those outcomes, which will help guide other pre-college engineering programs. Further research in the area is planned, and will benefit from the findings of this earlier survey.

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)0,004
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,023

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,017
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0000,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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 Opus0,040
Tête enseignante GPT0,274
Écart entre enseignants0,234 · 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'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

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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