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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".