Increasing first-year student motivation and core technical knowledge through case studies
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".