Transforming, Creating, and Executing a Brand New Frist Year Engineering Materials Course
Bibliographic record
Abstract
The transition from traditional lecture-style teaching to a flipped learning experience at the University of Calgary’s Schulich School of Engineering first-year program has been documented and detailed in various publications. This shift in teaching pedagogy aims to enhance engineering education by fostering comprehensive understanding, critical thinking, problem-solving skills, and active learning opportunities. In Fall 2023, this model extended to a materials engineering course, introducing the subject earlier into the curriculum. With a large student capacity, collaborate learning is prioritized in studio sessions, supported by visualization software and 3D printed models. The course structure includes online video lectures, quizzes, and an interactive textbook alongside in-person lectures and studio sessions. Active learning sessions feature hands-on experiments, enabling students to apply theoretical concepts practically. Assessments include quizzes, worksheets, and summative exams, with a focus on student engagement and understanding. Course management emphasizes preparation, communication and support, with a dedicated team overseeing coordination and grading. Initial evaluation indicates positive student engagement and perceived learning gains, though thorough evaluation is ongoing. Key learnings include the importance of proactive student engagement, structured guidance in problem-solving, and refining assessment methods. Overall, the flipped model has positively impacted student experience and highlighted the significance of materials engineering in engineering education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".