EPICS: Meeting Outcomes with Multidisciplinary Student Teams
Bibliographic record
Abstract
Engineering Projects in Community Service— EPICS — is a service-learning program that wasdeveloped nearly twenty years ago at Purdue University.Under this program, undergraduate students inmultidisciplinary teams earn academic credit for longtermprojects that solve technology-based problems forlocal or global community service organizations. TheEPICS model has been implemented at 23 universities inNorth American and on other continents. With itsemphasis on the start-to-finish design of significantprojects that will be deployed by the communitycustomers, EPICS addresses many of the programoutcomes mandated by ABET and the CEAB and, morebroadly, to meet the Washington Accord graduateattributes. This paper describes the curricular andassessment procedures and documentation that have beendeveloped to enhance and evaluate the students' abilitiesto meet outcomes including functioning onmultidisciplinary teams; communicate effectively; andunderstand the impact of engineering solutions in aglobal and societal context.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".