TEACHING THE FUNDAMENTALS OF CIVIL ENGINEERING MATERIALS THROUGH EXPERIENTIAL LEARNING
Bibliographic record
Abstract
This study presents an overview of a civil engineering materials course curriculum at Carleton University developed by the authors. The curriculum aims to move away from traditional civil engineering materials courses, which focus heavily on concepts related to material science, and instead concentrate on concepts that are more relevant to today’s practicing civil engineers. The rationale, application, and analysis of the integration of these concepts through an advanced application of case-based and experiential learning is discussed. Central to this new course curriculum is a hands-on experiential learning activity on the construction and experimental testing of reinforced concrete beam specimens in lab sections of approximately 25 students. The goal of the lab is to provide students with a hands-on learning experience and use this as a tool to cover advanced topics related to civil engineering; for example, environmental sustainability and resilience. The assessment of the students’ understanding of the concepts taught in class were performed through the use of an anonymous questionnaire distributed at the end of the course and through traditional examination and assignments. Results of the survey were compared between classes who engaged in the advanced experiential learning laboratory and those who did not. The results demonstrate that after introducing experiential learning into the course curriculum, students were more likely to form an educated opinion on the potential sustainability of a material. Experiential learning is shown to be a valuable tool for engineering education that, when used efficiently, can seamlessly incorporate newly emerging engineering concepts to ensure that graduating students are equipped with the knowledge and tools they require to be competitive in the job market. The relation of the course to contemporary accreditation of Graduate attributes is discussed at length along with critical information regarding the effectiveness of balancing student engagement in STEM subjects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".