Improving Classroom Engagement to Maximize Learning in an Interdisciplinary Dual Faculty Capstone Experience at the University of Manitoba
Bibliographic record
Abstract
One of the most effective and efficient ways for an engineering program to facilitate compliance with the Canadian Engineering Accreditation Board (CEAB) accreditation criteria is through capstone design projects and courses [2]. Currently, The University of Manitoba Faculty of Engineering has several capstone design courses; however, each is independently focused on its own respective discipline. The resulting educational experience for students, though rigorous and challenging, is maintained within the boundaries of the students’ engineering discipline, thereby neglecting to provide the opportunity for students to work with people from multiple disciplines and across different faculties. This method of education, where students work in isolation, arguably does not reflect real world engineering. Through internal focus group meetings, program representatives from the Faculty of Engineering at The University of Manitoba agree that the capstones should be more reflective of real life situations. Interdisciplinary courses are most important because they “…articulate the difference between educational problems and workplace problems” [1]. Hence, to allow “(students) persons from different disciplines to work collaboratively and are integrated to combine their knowledge to solve a problem” (sic)[4], interdisciplinary capstone courses are essential to a rounded engineering education. Furthermore, teaming with the Faculty of Architecture will provide several benefits for both facilities such as: develop lifelong learning patterns; foster cooperative and collaborative team relationships; and, allow both facilities to learn the other’s cultures and technical languages.Since 2016, The University of Manitoba has presented research papers at the Canadian Engineering and Education Association (CEEA) conferences on the development and future assessment of an interdisciplinary capstone course. These papers have shown the evolution of the course from a multidisciplinary engineering program to an interdisciplinary Engineering and Architectural dual faculty offering. The course was launched in January, 2018, and will be evaluated through the winter session and into the fall.This paper, will explore, define, and explain how the proposed new engineering/architecture interdisciplinary capstone and dual faculty course will be developed, highlight the early stages of its initiation, describe the ongoing implementation, outline how the performance of the new course will be evaluated, delve into how the new course will be improved to make it more meaningful and practical to both faculties and students, and; discover how engagement can improved student learning.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".