Student Perspectives on Employability Skills in Liberal Arts Programs: A Canadian Case
Bibliographic record
Abstract
The purpose of the study was to gain further understandings of undergraduate students’ perspectives on employability skill development in the liberal arts programs, as well as the perceived influence of the identification of employability skills in course curricula on undergraduate students’ self-efficacy. Building on the results of a prior study on faculty perspectives in the liberal arts on employability skills, we also explored the ways students’ and faculty members’ perspectives were in alignment. Purposive sampling was used to select the undergraduate courses from three different undergraduate programs at the research site. Three relatively high enrolment courses were selected based on two key criteria: must be a second or third level undergraduate course and must be in a non-professional program in the liberal arts. Non-professional programs were considered programs where the students do not graduate with a specific professional designation or applied program. Two methods were used to collect the data for this project: an employability skill inventory and a survey distributed to students (N = 131). Course syllabi were also obtained to map employability skills. The findings of this study provide further insights with respect to the ways that employability matters to students and the ways in which students expect their instructors and institution to play a role in their employability development. Conclusions of this study underscore the need to consider bridging the disconnect between expectations of the students, the institution, and the faculty regarding employability development.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".