Identifying curriculum factors that facilitate lifelong learning in alumni career trajectories: Stage 2 of a sequential mixed-methods study
Bibliographic record
Abstract
Abstract This research paper presents results from the second stage of a sequential mixed-methods study exploring the impact of undergraduate curriculum on lifelong learning orientations in the context of varying alumni career trajectories. Lifelong learning mindsets and skillsets are essential for graduates of engineering programs as they grapple with an array of sociotechnical challenges and unpredictable career paths. Previously, we used interview findings, in combination with a literature review, to develop a conceptual framework and alumni survey that address several related constructs: career trajectories, workplace learning orientations and undergraduate learning orientations, curricular and extra-curricular experiences and perceptions, and pre-university characteristics. The survey was designed to address the following descriptive and explanatory research questions within the larger study: RQ1) How can we characterize individuals' lifelong learning motivations and strategies before university, during university, and in their current workplace context? RQ2) What changes in lifelong learning orientations can we observe between these time-periods? RQ3) What influences do curricular experience factors have on lifelong learning orientations? We recruited alumni who graduated between 1991 and 2020 from two engineering departments at our institution to participate in the survey and received 279 complete responses. We found significant differences in lifelong learning motivations between the undergraduate and workplace stages (increases in the importance of one's interest in the context or activities and in achieving success; a decrease in the influence of avoiding failure). We also found correlations between undergraduate and workplace learning approaches in terms of tendencies towards memorizing information, understanding through making connections, or taking more proactive approaches.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".