DEVELOPMENT OF “INTRODUCTION TO ENGINEERING” COURSES FOR FIRST YEAR ENGINEERING STUDENTS: A FOCUS ON STUDENT ATTITUDES
Bibliographic record
Abstract
Many students entering an engineering program have a strong appreciation of the importance of math- and science-based skills for their future career as an engineer, but often have little grasp of what it means to be entering a professional college. For this reason, many engineering programs in Canada include some form of an “Introduction to the Engineering Profession” in their first-year program. The University of Saskatchewan’s College of Engineering has been working toward the launch of a completely redesigned first year program. This project has afforded the College an opportunity to apply a novel and transferable approach to shaping this “Introduction to the Engineering” experience. The structure of the proposed new first year program has allowed for short and intensive “Introduction to Engineering” modules, which bookend each of the regular session terms. This timing makes them an orientation for the program, allowing for timely deep dives into matters of importance to engineering students: study skills, time-management, teamwork, self-assessment, support services, student well-being, ethics, academic integrity, and health and safety. The timing of the modules also allows for completion of term-long assignments and reflection on both personal and academic growth. This paper describes the process employed to develop the course learning outcomes, schedule of topics and activities, and syllabi. The process focused on over-arching target attitudes, such as “I am on the path to becoming a professional”, and ensured constructive alignment between these attitudes and the learning outcomes, learning activities, and assessment. The nature of the process made it easy to clarify what was essential to include in the courses, and to make a compelling case for the importance of the courses in the context of a myriad of foundational technical topics.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".