How to Design a Design Project: Guidance for New Instructors in First and Second Year Engineering Courses
Bibliographic record
Abstract
Abstract The development of a resource tool for design project instructors in first and second year PBL coursesAbstractEngineering programs throughout North America continue to add curriculum approaches that useproject-based learning (PBL). 7 Atlantic Canadian universities share a common first two yearengineering program, and all have begun to implement a design-project core of coursesthroughout all common semesters. One of the difficulties in implementing the courses is thecomfort level of instructors with the teaching methods. This problem is exacerbated by thewidely different class-sizes and physical resources of all 7 campuses.This paper describes the development of a set of teaching resources to help aid the adoption ofnew project-based learning approaches. While the intent of the project was to specifically aidfaculty in the 7 target programs in Nova Scotia and Prince Edward Island, this problem is seen inmany universities where faculty are unfamiliar with PBL teaching approaches, and thus arereluctant or resistant to introduce these to their classes. The resource approach is to developmodular content within six major categories, a) active learning class structures, b) in-classapproaches, c) active learning assignment bank, d) evaluation methods, e) case studies andexample projects, f) problems encountered and lessons learned.Each of the major topics was selected based on the expectation of the needs of an instructor on aday-to-day basis while teaching. The project demonstrates an initial set of content in eachcategory. Since it was assumed that a fixed content bank would quickly become obsolete,approaches to ensure that the content is continually updated by faculty were implemented at theoutset. The initial implementation has been demonstrated in on-line course managementsoftware, and can be utilized in many university engineering faculties. A report of initialfeedback from teaching faculty to the tools will be part of the presentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".