Capstone 101: A Framework for Implementation of an ABET-compliant Capstone Sequence
Bibliographic record
Abstract
Abstract Capstone 101: A Framework for Implementation of an ABET-Compliant Capstone SequenceThe ABET Criteria for Accrediting Engineering Programs states, “Students must be prepared forengineering practice through a curriculum culminating in a major design experience based on theknowledge and skills acquired in earlier course work and incorporating appropriate engineeringstandards and multiple realistic constraints.” The ABET Criteria for Accrediting EngineeringPrograms states “Baccalaureate degree programs must provide a capstone or integratingexperience that develops student competencies in applying both technical and non-technicalskills in solving problems.” Many programs already have a course in place that was designed toserve the purpose of providing a clinical experience for students prior to graduation, but thevision for these courses is inconsistent at best when the population of programs in the UnitedStates is considered as a whole. Some institutions implement the course as a single semesterexperience, some as a two semester experience. Some programs concentrate all students on asingle project; some programs employ multiple projects from which student teams choose. Theteaching burden for courses of this nature is not congruent with a traditional lecture or laboratorycourse. Some Universities recognize this difference, others simply treat it as just another course,despite its’ key role in retaining accreditation and the large time commitment required for auseful experience for the students.This paper defines a notional structure for implementing a modern capstone experience wherenone exists or where a new course paradigm is desired, based on the experiences of manyprogram instructors from across the United States and Canada. This includes data andexperiences described in seminal published papers, as well as the personal experiences of theauthors. The material presented will also serve as a training aid for faculty new to this type ofcourse delivery with suggestions for minimizing effort while maximizing quality and realism ofstudent experience.
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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 | 0.054 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".