Bibliographic record
Abstract
Purpose: The purpose of this research is to develop a set of self-efficacy surveys for use in the graduate attribute and continual improvement (GA/CI) process to address the Futures of Engineering Accreditation Pathways (FEA) steering committee’s “Futures of Engineering Accreditation Path Forward Report” recommendations. Methods: We build on a 2010 project to develop a self-efficacy survey for accreditation purposes. The study involved an annual survey of final-year engineering students from 2010 to 2024 to assess the reliability of the survey questions. Results: The majority of the survey’s GA categories showed strong internal consistency; however, the results identified a need to improve the survey question in three categories. Changes to the survey structure were identified to improve participant engagement and alignment with the FEA’s Full Spectrum Competency Profile. Implications: This work has the potential to add students’ educational experience in the GA/CI process and support a move to full outcomes-based accreditation.
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.
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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".