EXPERIENCE WITH A SMALL UAV IN THE ENGINEERING DESIGN CLASS AT CAPILANO UNIVERSITY – A NOVEL APPROACH TO FIRST YEAR ENGINEERING DESIGN
Bibliographic record
Abstract
Capilano University offers a very successfulfirst-year Engineering Transfer and EngineeringTransition Diploma program. A key learning experiencefor students of both programs is a one-semesterEngineering Design course, in which the instructor leadsthe student through a practical design project, applyingengineering design principles that are presented in thecourse. In 2014, a new project design theme wasintroduced, specifically working with small UnmannedAerial Vehicles (UAVs). The teaching methodologycombined both lecture and student led learningexperiences. Student teams of 4 were provided a commonassignment to carry out a field investigation using a UAVand video camera to simulate an industrial application.With experience gained on a PC based flight simulator,all teams successfully completed their designs, carried outtheir UAV flights and documented their results. Studentsdeveloped project management and communication skillsearly in their engineering education. This innovativeapproach in first year provides students with immediateexposure to the practical limitations that constrainengineering design. The teaching methodology isexpected to result in graduate engineers who havestronger skills in teamwork, communication, and designcapability. This first year teaching methodology hasshown significant promise as demonstrated through thequality of the design projects, as well as positive feedbackfrom the students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".