Redesign of Freshman Electrical Engineering Courses for Improved Motivation and Early Introduction of Design
Bibliographic record
Abstract
The student experience during the freshman year has been recognized as one of the keys to not only attracting more students into engineering and improving retention, but also to forming some significant attributes of successful engineering graduates. Portland State University is an urban university, and its Electrical and Computer Engineering (ECE) department serves a relatively large and very diverse student population including a large fraction of transfer and part-time students. Traditionally, all engineering disciplines within our Maseeh College of Engineering and Computer Science had a similar freshman year curriculum. The common entry course – Engineering and Applied Science (EAS) 101 – served as the cornerstone along with one or two additional courses which were more discipline specific. In ECE these two courses covered introduction to programming and digital logic, with the former taught by the Computer Science (CS) department and the latter by ECE. There were a number of reasons why we decided to redesign our undergraduate curricula. Through our own assessment and feedback from employers and alumni, several programmatic issues were identified: a) insufficient programming skills, b) introduction to design only in upper-division courses, c) weak communication skills. At the same time, many schools across the United States were reducing the credit load in Electrical Engineering (EE) to 180 credits, and we had started feeling pressure from our students and prospective students as well. This prompted our examination into ways of rationalizing and potentially reducing the number of courses. Finally, we wanted to make our program more attractive to undecided and traditionally under-represented groups of students. We realized that solutions for many of the identified issues might be found by focusing on how we introduce freshman students to electrical and computer engineering fields.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".