Preparing Engineering Students for Their Profession - A Novel Curricular Approach
Bibliographic record
Abstract
Abstract This Work-In-Progress (WIP) paper describes a core engineering course series that has been developed by the University of South Florida (USF) Electrical Engineering (EE) Department that seeks to provide a comprehensive approach to prepare engineering students for their professional careers and improve student retention. The required Professional Formation of Engineers (PFE) course is offered as a series of three 1-credit courses, which span the Sophomore/Junior years, and provide a bridge between a Foundations of Engineering course, which is required for all USF Engineering Freshman students and two required senior-level EE Capstone Design courses. The purpose of this paper is to share content information and lessons learnt on the PFE course series model, and how the course helps students develop critical competencies identified by the National Association of Colleges and Employers (NACE), engage with engineering industry representatives, researchers, and faculty, and understand engineering ethics from a practical/professional perspective. The theory of action-state orientation is utilized. Research demonstrates that action-oriented college students attain higher grade point averages and engage in more extracurricular activities than state-oriented students. In the PFE course series, students create and maintain a personalized undergraduate career roadmap using experiential learning activities. Students set goals, and track and assess their individual progress to achieving those goals. They use Risk Management processes to resolve ethical case studies and demonstrate real-time critical thinking and problem- solving skills during a mock Senate Ethics Hearing. Students also choose technical areas to research, and work in groups to develop research proposals, patent applications, and business plans. As a result, students learn to apply ethical perspectives and consider the full implications of unethical practices, develop valuable professional competencies, communicate with a diverse set of stakeholders and audiences, and identify a technical area of interest and work as a group to create and present a technology development proposal and business plan that meets a community need. The assignments and projects in the PFE course series directly address ABET Outcomes 4 and 5. The professional development and experiential learning activities required by the course series provide an opportunity for students to demonstrate proficiency in other ABET Outcomes as well.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".