Are Work-Integrated Learning (WIL) Students Better Equipped Psychologically for Work Post-Graduation than Their Non-Work-Integrated Learning Peers? Some Initial Findings from a UK University.
Bibliographic record
Abstract
Work-integrated learning (WIL) provides an opportunity to develop the skills, knowledge, competence, and experience, which increase employability and lead to more satisfying careers. Research indicates that WIL results in improved academic- and occupationally-related outcomes. However, there is a paucity of quantitative research examining the psychological impact of WIL. The study aimed to determine whether students who pursue WIL in the UK, differ significantly in terms of self-concept, self-efficacy, hope, study skills, motivation, and procrastination than students who have not participated in WIL. The methodology used a cross-sectional analysis of a large sample (n=716) of undergraduate students at the University of Huddersfield, UK. Results showed significant differences predominantly centred upon measures which pertain to students’ confidence in setting and attaining goals. The increased hope and confidence in goal attainment suggest that gaining work experience perhaps enhances the ability to set and achieve goals once in the work force. (Asia-Pacific Journal of Cooperative Education, 2013, 14(2), 117-125) \nKeywords: Employability; Psychological factors; Work-integrated learning; Placement; Confidence; Self esteem
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".