Effects of Online Career Training Modules on Undergraduate STEM Students’ Career Readiness Perceptions
Bibliographic record
Abstract
A challenge in higher education is promoting the development of employability skills to meet employer expectations post-graduation. This study aimed to determine the effect of an optional online career readiness training resource, consisting of four training modules, on students’ career readiness perceptions [Training Module (n=102) versus untrained Control (n=58)]. Training Module students reported increased overall job readiness and understanding of career opportunities, translation of skills to the workplace, employer expectations and the ability to meet those expectations compared to the Control Group. Additionally, career readiness perceptions were negatively correlated with stress levels, indicating that the more prepared students felt regarding career readiness the lower their stress experience. Both development of key employability skills and traits and career readiness perceptions were positively correlated with a deep learning approach, highlighting the importance of learning approach in post-graduation preparation. This study was conducted during the COVID-19 pandemic and students’ perceived stress levels increased during the academic semester (P<0.05), however, there was no difference in overall stress levels between the Training Module and Control Group. Additionally, within the Training Model Group perceived stress levels were inversely correlated with career readiness perceptions, including identifying a career path, awareness of workplace expectations, confidence in meeting employer expectation and overall job readiness. Collectively, this study demonstrates the value of additional career planning training to support students transitioning out of undergraduate programs and identifies the impact of both learning approach and stress on students’ overall perceptions of their job readiness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".