The impact of university co‐curricular activities on competency articulation proficiency: A mediated model
Bibliographic record
Abstract
Abstract To succeed after graduating, university students must develop and communicate their career‐related competencies to hiring managers or graduate admissions committees. Co‐curricular activities (e.g., volunteering, mentoring) coupled with reflection can facilitate students’ career exploration and help them understand, develop, and apply their career‐related competencies. Yet, as a scientific community, we need to learn more about the role of co‐curricular programming in helping students to effectively articulate their learned competencies. We draw on past research to develop and test a model of university student competency articulation proficiency. A serial mediation model predicted students’ learning goal orientation would influence their co‐curricular engagement, which, in turn, would predict career exploration and decision‐making self‐efficacy and self‐reported competency articulation proficiency. We surveyed 126 students enrolled in co‐curricular programming at a university in North America. Results largely supported the hypothesized model. Learning goal orientation, directly and indirectly, affected career exploration and decision‐making self‐efficacy and competency articulation proficiency.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".