CAD / ARDUINO INTRODUCTORY WORKSHOPS IN FIRST YEAR ENGINEERING PROJECT-BASED COURSE A WAY TO GET STUDENTS TO DESIGN AND BUILD HIGH QUALITY PROTOTYPES
Bibliographic record
Abstract
As educators, there are several challenges that we need to overcome during the first-year of our engineering programs. We need to integrate students into the world of engineering and introduce them on practical aspects. We have to connect them with basic notions in various engineering fields and complementary disciplines. We have to start developing their design skills and make them able to conduct design projects despite an undeveloped level of knowledge. We want to make them able to design and build high-quality prototypes. At UQAR, the solution goes through a multi-disciplinary project-based course offered at the first term. This course brings together students from mechanical, electrical and electromechanical engineering programs which makes possible to form teams of students with varied skills from different backgrounds (technical and natural sciences). In 2018, the proposed project was the production of a stand-alone and collaborative mobile platform. This platform or robot receives an order for an item, picks up and transports a shelf to the deposit station, delivers the item, returns the shelf to its place and returns to the starting position, all while avoiding obstacles on the way. To fulfill such an ambitious mandate, basic notions of CAD, electronics and programming are needed. Our innovation is the introduction of workshops at the beginning of the term. Workshops are an important part of our recipe to enable students to overcome the design challenge. The mechanical workshops include four sessions related to mechanical CAD (modelling, sheet metal, assembly and drawing) and 3D printing. Arduino workshops include three sessions on how to use the Arduino IDE programming environment and incorporate some programming notions. Specialized material was produced concerning the introduction to microcontrollers and the Arduino platform. A set of specialized parts was provided to the students. Several demonstration examples and activities have been produced on digital inputs and outputs, analog inputs, pulse width modulation outputs, ultrasonic distance sensing, DC motors and H-bridges, servomotors, tracking sensors lines, wireless communication modules, mini-keyboards and LCDs. The workshops support the projects directly and allow students to carry out relatively complex projects in the first year despite their limited engineering knowledge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.152 | 0.079 |
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".