Implementing project-based and experiential learning in the Aerospace Engineering program at Concordia University
Bibliographic record
Abstract
Experiential learning can be defined as“learning from experience or learning by doing”. Theeffectiveness of the experiential learning techniquedepends on both the design and the implementation of the experience. The learning experience must be carefully designed so that students do not learn by rote but rather are obliged to self-teach, discover, and use engineering judgement to arrive at conclusions. Student interest and their perception of the project as being authentic and representative of the “real-world” is important for engagement. In this paper, the authors discuss the development and implementation of experiential and project-based learning in the new undergraduate aerospace engineering program at Concordia University.The paper describes a unique series of experientiallearning experiences that have been implemented in thefirst, third, and final years of the program. Two of theauthors are former aerospace industry design engineers,and a unique feature of the program is a blend of fieldbased experience and classroom-based learning made possible by collaborations with industrial partners and organizations external to the university
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".