Work in Progress: Curriculum Revision and Classroom Environment Restructuring to Support Blended Project-Based Learning in First-Year General Engineering Laboratory Courses
Notice bibliographique
Résumé
This work-in-progress report details the restructuring of a three-quarter first-year general engineering laboratory course sequence ending in a term-long cornerstone design project.Motivated by a taskforce implemented in 2015 to improve the first-year common curriculum, this development effort affects the first two quarters of this three-quarter, first-yearprogram laboratory course sequence.Faculty representatives from all engineering departments in the college were assembled to address three goals.The first goal was the establishment of a course structure emphasizing professional skills and engineering design.Second was the creation of a database of "mini-projects" to be integrated into the new course structure.The third goal was the establishment of a blended learning environment which uses web-based lectures and assessments in conjunction with hands-on, problem-based-learning laboratory activities.Three design-focused mini-projects were piloted during the fall and winter quarters of the 2016 -2017 academic year.A professional skills-focused "micro-project" ran for the first three weeks of the fall quarter, followed by seven weeks of a design-focused "mini-project".Pilot sections in the winter quarter began with a different seven-week mini-project followed by three weeks of another professional skills-focused micro-project.The first three mini-projects developed for this effort were titled: Robot Instruments, Heat Engine, and the Supercap Car Challenge.During the fall and winter quarters, students in the pilot sections were given self-efficacy surveys before and after their projects based on a Likert-type scale.These gauged their impressions of the projects, and self-evaluated their relevant knowledge and abilities before and after the projects.Early results presented in this paper indicate an improved level of student satisfaction with the new course structure and the pilot mini-projects.Table 1: First-year engineering laboratory course sequence areas of emphasis. Engineering Professional SkillsTechnical communication, organization and presentation.Ability to work in teams.Time management and planning.Professional skills for co-op (resume, interviews, etc.).Project management (manage tasks, budget, etc.).How to use research resources.How to critically evaluate information (found online, in books, articles, etc.).Ability to interact with a diverse audience.Understand societal factors impacting engineering (aesthetics, ethics, sustainability, manufacturability, etc.).The business cycle of engineering; role of entrepreneurship.Different engineering disciplines.Ability to define engineering project success and/or performance enhancement.Ability to adjust to different cultures and understand different global needs and constraints.Understand role of research in engineering; gain experience in research.Engineering Fundamentals Programming and logical thinking.Use of mathematics in the design process.Use of basic science in the design process.Integration of science into the design process.Ability to decompose problems into sub-problems.Ability to define tasks in a systematic manner.Understand limits of measurement, basic statistical analysis, error and uncertainty, and interpretation of data.Use various measurement and fabrication tools and technologies.Understand the design process.Use CAD, modeling, and/or visualization tools.Ability to do and to design experiments, gather data, analyze, report and present data.Understand relationships between inputs and outputs in systems.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».