Commentary on the design of an alumni survey that investigates the relationships between undergraduate experiences and lifelong learning
Bibliographic record
Abstract
Lifelong learning is essential for engineering students and practicing engineers but challenging to teach, assess, and measure as a graduate attribute. This paper reports on the development of an alumni survey that measures the long-term impacts of undergraduate curricular experiences for lifelong learning across a diversity of alumni career trajectories to help undergraduate engineering programs learn about their long-term impacts and make strategic improvements. The survey presents a novel method for characterizing alumni career trajectories in terms of career transitions and the relatedness of alumni career activities to their undergraduate programs. Items probe specific dimensions of lifelong learning motivations and strategies and allow for the bivariate analysis of relationships between these variables, career factors, and undergraduate experiences. Combining items into scales proves to be challenging due to the variability in alumni characteristics and experiences. Future research should investigate how engineering students and alumni are motivated by a combination of intrinsic and extrinsic factors, and what implications these tendencies have for their career development and ongoing learning.
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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 | 0.051 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.049 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".