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Enregistrement W2995653961 · doi:10.18260/1-2--31887

Problem-Based Lab Education: Redesign of a Senior Year Chemical Engineering Lab Course to Promote Autonomy, Critical Thinking, and Problem-Solving Skills

2024· article· en· W2995653961 sur OpenAlexaff
Gabriel Potvin

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueProblem and Project Based Learning
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésAutonomyCritical thinkingCourse (navigation)Computer scienceEngineering managementEngineering educationMathematics educationEngineeringPsychologyPolitical science

Résumé

récupéré en direct d'OpenAlex

Abstract This abstract is submitted to the 2019 ASEE PNW conference to be considered for an abstract-reviewed presentation (not a full paper). Upper-year engineering students are quite adept at collecting and organizing experimental data as part of prescribed laboratory exercises. Although these labs provide good opportunities to develop analytical skills, they offer few avenues to apply creative problem-solving or experimental design skills. Furthermore, when students are faced with technical difficulties in the lab, many rely on TAs or instructors to solve them and are reluctant to, or simply assume that it is not up to them to, attempt a solution themselves. To alleviate some of these issues, building on previous work, the senior year chemical engineering lab course at UBC was redesigned using a Problem-Based Learning (PBL) framework and greatly reduced formal supervision, with the aim to promote resourcefulness and problem-solving, troubleshooting, and critical thinking skills. The structure and deliverables of this course will be described, student performance assessed, and the feedback of students regarding this approach will be presented. In this course, student teams perform a single multi-week PBL lab per term. An instruction manual for the relevant equipment is provided, but with no suggested experimental design or data collection protocol. Open-ended industrially-relevant problem statements, consisting of design, optimization or investigative objectives, are provided as suggested context, but teams are not bound by these statements and are encouraged to propose alternative problems based on their interests. Before beginning their experimental work, each team prepares a formal project proposal, which includes a comprehensive review of background theory, experimental design, budget an environmental impact assessment, and safety audit of the procedure. At the beginning of each experimental session, teams provide an updated work plan outlining the work performed to date, the work to be performed during that lab session, and any changes to the original proposal deemed necessary, mirroring industrial protocols. Students present their work both orally and as a written report at the end of the session. To increase the independence and self-reliance of the teams, TA and instructor involvement is greatly reduced. TAs are only present in the lab for the first hour of each experimental session to review and evaluate the work plan, ensure the students are prepared, and that they are able to perform their experiments safely. TAs are instructed not to directly answer technical questions and are there only as observers. This forced students to make their own decisions and judgements, troubleshoot any technical issues or unexpected results, and experiment with the process themselves. When initially presented with the course structure, many students were apprehensive, with some voicing their displeasure at having traditional supports and direction removed. At the conclusion of the course however, student feedback, collected through a survey, was overwhelmingly positive. The vast majority of students reported benefitting from the independence and responsibility for their work, with the majority self-reporting improvements in confidence in their engineering judgement and critical thinking skills. A comprehensive analysis of student feedback will be presented.

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)0,003
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut 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: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0030,001
Science ouverte0,0030,003
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0130,006

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 Opus0,008
Tête enseignante GPT0,297
Écart entre enseignants0,289 · 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'étudeSans objet
Domainenon disponible
GenreMéthodes

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
Publié2024
Routes d'admission1
Résumé présentoui

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