INTEGRATING LEARNING OBJECTIVES IN A MULTI-SEMESTER SUSTAINABLE CONSERVATION DESIGN PROJECT FOR FIRST-YEAR STUDENTS DURING A PANDEMIC
Bibliographic record
Abstract
Project selection for first-year design courses can be complicated by the limited skill level of students in their first semester of an engineering program and the scalability required for multiple sections and large classes. Additionally, the project must address the course's learning objectives and provide a sense of authenticity to help students understand the role of engineers in society. Afirst-year design course can be seen by students as the ‘introduction to engineering,’ enabling them to decide whether to pursue engineering as a profession or not. In addition to the already taxing demands imposed on a project for a first-year design course, students at the University of Prince Edward Island completed a design project encompassing two engineering courses andcontributed to a scientific research study on bat conservation. Partnering with researchers in the Atlantic Veterinary College, students designed, built, and installed bat houses equipped with sensors to remotely collect temperature, humidity, and the presence of individual bats within the colony. Constructing 21 bat houses promoted conservation efforts of bats across the province and taught students the critical role of engineers in a sustainable society. This paper presents a discussion on project selection for first-year design courses, how the learning objectiveswere met for two first-year design courses during a pandemic, and describe the community partner's role\ throughout the design project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".