The Adoption of a Capstone Assessment Instrument
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous research has shown that the adoption of effective innovations in engineering education typically lags behind awareness of those innovations, and this delay may be limiting the positive impacts of advances in engineering education. PURPOSEThis study investigates early adoption processes in detail in the context of an assessment instrument designed for use in capstone design courses.Research at this level of detail is needed to better understand adoption processes in general and to encourage spread of innovation in engineering education practice. DESIGN/METHODSemistructured clinical interviews were conducted with the developers and users of the assessment instrument and the educators who were introduced to it in a workshop.These interviews were analyzed thematically with respect to the diffusion of innovations theory. RESULTSThe qualitative, in-depth nature of the analysis revealed surprising diversity in the participants' perceptions of the assessment instrument.The most important features affecting their adoption were the participants' perceptions of its compatibility with their own and their institutions' values and goals.The complex and varied ways in which the participants were involved with capstone courses at their university were important in understanding their adoption decisions. CONCLUSIONThe findings are analyzed in terms of the context in which they arose, and their transferability to other contexts is discussed.The interactions between participants' perceptions and the specific context of their university's capstone program affected their adoption decisions, but these decisions are not easily characterized by existing theories or addressed by typical dissemination efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it