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Enregistrement W2594388294 · doi:10.18260/1-2--2045

Student Led Design, Build, Testing And Usage Of In Course Experimental Laboratories

2020· article· en· W2594388294 sur OpenAlexaffabout
Khosrow Farahbakhsh, Warren Stiver

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueEngineering Education and Pedagogy
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésCourseworkComputer scienceComponent (thermodynamics)Process (computing)RecipeSet (abstract data type)CurriculumCourse (navigation)Software engineeringTransfer (computing)MultimediaEngineering managementMathematics educationEngineeringProgramming languageOperating system

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 Student-Led Design, Build, Testing and Usage of In-course Experimental Laboratories Abstract Laboratory components of engineering courses are traditionally designed and assembled by either course instructors or laboratory technicians. Student’s involvement is most often passive owing to a detailed recipe style set of instructions and frequently recipe style report preparation in which even the relevant axes of figures have been predefined. Mass Transfer Operations (ENGG*3470) is a course that was introduced into the Environmental Engineering curriculum at the University of Guelph in 1998. A lack of facilities initially meant the course started without an appropriate laboratory component. Over the past four years the course has evolved through student designed, built and tested experiments as an integral component of their coursework. Currently, the students are responsible for choosing a mass transfer topic, selecting compounds involved in the mass transfer process, identifying most appropriate analytical techniques, designing, building and trouble-shooting the required apparatus, performing a minimum of two experiments and synthesizing the data in form of a laboratory report. Additionally, the students prepare a laboratory manual that is then used by other students to conduct the particular experiments. Our experience over the past five years indicates that such an approach is not only manageable but also provides the students a unique opportunity to sharpen their design, research as well as communication skills while learning the fundamentals of mass transfer operations. This paper describes the evolution of this approach within the third-year mass transfer course and provides an assessment of its effectiveness on student’s learning. Introduction It is now generally agreed that involving the students in the process of learning and knowledge construction promotes more in-depth understanding, better retention of concepts, increased interest on the subject matter among the students, and stronger problem solving skills. Several approaches have been practiced by educators to ensure meaningful participation of students in learning including problem-based learning1, “learning by doing”2, and “project-oriented education”3 to name a few. All these approaches emphasize a “learner-centered approach” and a move from a “content-based” to a more “context-based” education4. In addition to sharpening student’s laboratory skills, most undergraduate lab-based courses are used to promote some type of hands-on learning. In conventional laboratory course students are provided with detailed instructions on how to perform the work and, in many cases, how to analyze the data. The experimental setup is typically fully laid out by laboratory technologists or graduate teaching assistants and analytical equipment is checked, troubleshoot and calibrated with little or no input from the undergraduate students. In most cases such an approach to undergraduate laboratory experiments is driven by the need to move a large number of students through a lab with limited resources and within a prescribed time period. There are several limitations with the conventional approaches to laboratory exercises in undergraduate courses. Conventional in-course laboratories do not encourage student enquiry

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: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,247

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 Opus0,040
Tête enseignante GPT0,314
Écart entre enseignants0,274 · 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'étudeExpérimental (laboratoire)
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'admission2
Résumé présentoui

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