Learning beyond the lecture room: Do placements help students learn about themselves and for themselves?
Bibliographic record
Abstract
Learning in the workplace, particularly through placements and internships, is increasingly recognised as a means to enhance and advance the skills, knowledge and competence that students develop in an academic setting (e.g. Bates, 2008; Boud & Falichikov, 2006; Costley, 2007; Crebert et al., 2004). However, the psychological outcomes of work integrated learning are not yet fully established and have also been contested by some (Allen & van der Velden, 2007). Aim. In this emerging area of research interest, the study aimed to determine whether differences in self-concept, self-efficacy, hope, and motivation exist between students who have taken part in a placement versus those who have not. Methodology. The following valid and reliable measures were collected, post placement, from a large sample (n=956) of undergraduate students: Trait Hope Scale (Snyder et al., 1991); Self-Description Questionnaire III (Marsh & O’Neill, 1984), a measure of self-concept; College Academic Self-Efficacy Scale (Owen & Froman, 1988); Motivated Strategies for Learning Questionnaire (Pintrich et al., 1993), a measure of study skills and motivation. The methodology utilised for this study was designed by Drysdale et al. at the University of Waterloo (Canada), who led an international comparative research study of which our study forms a part. Results. Students who participate in placements had significantly higher trait hope, agency, and lower test anxiety than their non-placement counterparts. However, there appeared no significant differences in the way in which placement and non-placement students approached their studies, in terms of study skills or the degree of procrastination reported.
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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 | 0.003 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".