Adding Flexibility and Hands-On Experiences while Minimizing Sequential Gaps in the ME Curriculum
Bibliographic record
Abstract
Abstract Adding Flexibility and Hands-On Experiences while Minimizing Sequential Gaps in the ME CurriculumThe Department of Mechanical and Materials Engineering at the University of Denverrecently undertook a strategic planning process to identify critical changes to the programneeded to address how the modern BSME degree is applied or will be utilized in thefuture. Three initiatives were implemented as a result of this process: (1) increase thenumber of hands-on experiences to differentiate from online curricula, (2) add flexibilityin general and in technical electives to allow students to tailor their educationalexperiences to their long term goals, and (3) minimize gaps between courses intended tobuild on each other. Hands-on experiences now exist in all but one quarter of our four-year curriculum. The experiences incorporate open ended design problems as well asthoughtfully constructed laboratory experiences. Flexibility has been added by allowingstudents to select three to four courses from pre-approved math and science courses, byopening up the timing of these courses as well as general educational requirements withinthe four year curriculum, by doubling the number of mechanical engineering technicalelectives available, and by allowing students to take technical electives from any of ourthree engineering programs (Mechanical, Electrical, and Computer Engineering).Moreover in collaboration with the University of Denver’s Law School, our students cannow satisfy a technical elective requirement by taking the Law School’s Introduction toIntellectual Property course. Finally the faculty worked to identify all follow-on coursesand rearranged the curriculum to minimize the gaps between one class to the next.Our objectives are to increase ratio of the number of students depositing to the programto the number of students accepted to the program, to increase the persistent rate ofstudents, and to increase the depth of learning as measured by the Fundamentals ofEngineering Exam. An early measure of our success can be observed though theselection of discipline by our students at the end of a two year common engineeringcurriculum. The freshman who entered or program in the Fall of 2011 represent the firstwave of students to enter this new format. Roughly 85% of this population selected aBSME over BSEE and BSCompE. Prior to 2011, roughly 50% of the students chose theBSME program. Additionally, during the past two years, our FE pass rates exceeded90%. These early findings suggest that it is possible for ME curricula to accommodatethe direction and desires of engineering students while exceeding ABET requirements
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".