Psychological attributes and work-integrated learning: an international study
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore – on an international level – the relationship between work-integrated learning (WIL) and several psychological attributes (i.e. hope, procrastination, self-concept, self-efficacy, motivation, and study skills) believed to be important for a successful transition to the labor market. Design/methodology/approach – A between-subjects design was used with participants in one of two groups: WIL and non-WIL. The design provided data on the effects of the independent variable (WIL) on a number of dependent variables (attributes) across four countries. Data were collected via an online survey and analyzed using a series of ANOVAs and MANOVAs. Findings – WIL and non-WIL students in the four countries shared several attributes – however – significant differences also emerged. WIL compared to non-WIL students compared reported stronger math and problem solving self-concepts, yet weaker effort regulation and perceived critical thinking skills. WIL students were more extrinsically motivated than their non-WIL peers in three of the four countries. Female students in WIL reported being the most anxious compared to other students. Research limitations/implications – Self-reports to measure psychological attributes and the small sample sizes at some of the institutions are limitations. Originality/value – The positive relationship between participation in WIL and several aspects of positive self-concept are provided. In addition, data are provided indicating that overall there are more similarities than differences between WIL and non-WIL students on a number of psychological outcomes. Data also suggests that females who participate in WIL may be at risk for anxiety problems.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".