Using Professional Mentors For Capstone Design Projects At A Distance
Bibliographic record
Abstract
For over ten years, the University of Hartford's Department of Civil and Environmental Engineering has used professional engineers from the local region as mentors for capstone design projects.The mentor is asked to propose a candidate project, and if the project is selected by a student group, to oversee its technical direction.The mentors become role models for the students, and by allowing students to visit their offices and job sites, give the design teams a glimpse of engineers at work.Past course evaluations by both the students and mentors show a high degree of satisfaction with the experience.Recently, the department set up a web site that encourages alums to post information about themselves.Most cited recent or current job experience.Some were so interesting, that we thought they would make excellent capstone projects -except that they were not local.With a willing alum, and recognizing that we would have to use several forms of communication, we formulated a project using a mentor-at-a-distance.The project selected involved the design of a highway bridge located in Chesapeake, Virginia.Parsons, Brinckerhoff, Quade and Douglas, Inc. from Norfolk, Virginia, for whom our mentor worked, was responsible for designing the bridge.They provided site drawings, copies of specifications, and other design materials.Under the guidance of the mentor, the students designed an interior beam and the roadway slab, using AASHTO's (American Association of State Highway and Transportation Officials) 16 th Edition of the Standard Specifications for Highway Bridges, and VDOT (Virginia Department of Transportation) modifications to AASHTO's standard specifications.The results of a course assessment questionnaire indicate that engineering, communication and computer skills were enhanced while management skills were not.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.032 |
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".