Increasing first-year student motivation and core technical knowledge through case studies
Notice bibliographique
Résumé
Abstract In engineering programs with a common first year, students may feel like they are in Grade 13, rather than members of a fledgling community working towards entry into an exciting and impactful profession. Memorial University's Engineering One first year has three goals: 1. Educate students about what engineering is, in contrast to pure math or science. Students with good judgement, communication skills, and emotional intelligence; but lower math/physics self-efficacy, should become reassured that they can thrive. High-performing math/science students should become informed of other skills they may need to develop. 2. Inform students about the various disciplines, one of which they must select and enter in second year. 3. Prepare students for departmental specialization, with readiness in areas such as numerical literacy, ability to use spreadsheets, presentation and interpretation of data in graphical form, and ability to critically reflect on results. A course called "Thinking Like an Engineer" (TLE) has been designed, driven by a collection of case studies from different departments. We present big-picture engineering problems to students in an analytically tractable form. The case studies i) show how real-world needs are turned into quantitative engineering problems with constraints, ii) give global learners a sense of the problems they will be able to tackle with more depth as they move through the program and beyond, iii) provide a context in which to learn computer tools, especially Microsoft Excel, iv) provide opportunities to give formative feedback on graphical communication and data analysis, significant figures, estimation, basic statistical analysis, and so on. In contrast to "typical" first-year engineering courses, TLE is intended to connect course work to career goals for global learners and social conscience-driven students. The following methodology is proposed for case study development: 1. Set top-level goals for case studies at the Core (or equivalent) department level. 2. Engage junior co-op student "engagement partners" in the search for topics and relevant literature. Such students have proximity to the target audience in terms of maturity and technical ability. 3. Canvass faculty members for department-specific topics, while seeking interdisciplinary connections. 4. Connect engagement partners with faculty experts for first-draft technical vetting. 5. Focus group the first official draft by having • Core faculty work through it, ensuring connection with desired course outcomes. • Engagement partners' peers completing it, assessing time requirement and difficulty 6. Deliver within course, with reflection and continuous improvement enabled by student feedback. Case studies are conducted in a small group setting, supported by online resources. The current complement of case studies include an analysis of engine shaking forces, electrical utility load leveling with renewable energy, optimization of solar panels for hot water heating, route selection for a proposed highway using mass diagrams, and a coffee manufacturing study with a hands-on component. Surveys are being conducted to assess students' confidence in their understanding of the engineering approach to real-world problem solving, the technical areas related to the case studies, and their confidence and desire to persist in engineering.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».