Project-based Approach in a First-Year Engineering Course to Promote Project Management and Sustainable Energy Development
Bibliographic record
Abstract
To safeguard the environment and satisfy the energy needs of the present, without compromising the ability of future generations to do the same, sustainable energy development is urgently needed. This complex task is riddled with social, political, scientific, technical, and environmental challenges. Education is essential if we are to meet the energy demands of the world in the most sustainable manner available to us. Langara College offers a first-year engineering course that is meant to introduce students to engineering design and case studies, in addition to providing a brief glance on the history, ethics, and the different disciplines of engineering (APSC 1010–Engineering and Technology in Society). This paper demonstrates how this, or similar courses are optimized to raise awareness of the sustainability issues this planet is facing.Using a project-based learning approach that promotes teamwork and research, this course uses a variety of instructional methods including lectures, class discussions, and guest appearances by experts in their fields. Introductions to technical concepts, such as soldering, 3D printing, and microcontroller, are also addressed. The course culminates in a group project in which students are encouraged to select and research an issue on sustainable development. Several topics, which has already been briefly covered by the curriculum, include population, ecosystem, energy, and water.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".