Does work integrated learning better psychologically prepare British students for life and work.
Bibliographic record
Abstract
Work integrated learning (WIL) provides a plausible mechanism to develop the skills knowledge, competence, and experience (Bates, 2008; Boud & Falichikov, 2006; Collin & Tynjala, 2003; Crebert et al., 2004; Rhodes & Shiel, 2007), which increase employability and lead to more satisfying careers (Bates, 2008) in a competitive labour market (CBI/UUK, 2009). The occupational and academic impact of work integrated learning including better careers, salaries and degree outcomes are beginning to be established (Powell et al., 2008). In addition a range of personal skills and professional competencies have been cited as outcomes of work integrated learning, such as improved decision making, interpersonal and self management skills (Costley, 2007; Crebert et al., 2004), the application of theoretical knowledge in workplace environments, professional networking, professional behaviour, and leadership (Costley, 2007; Dreuth & Deuth-Fewell, 2002; Lizzio & Wilson, 2004; Rickard, 2002). \n \nHowever, the psychological outcomes of work integrated learning are not yet fully established or understood and have been contested by some (Allen & van der Velden, 2007). The impact of work integrated learning on traits such as hope, self efficacy and self concept have yet to be researched. \n \nAims and Objectives \nIn this emerging area of research interest, the aim of this study is an examination of the relationship between work integrated learning and the psychological variables believed to play an important role for graduate success in the subsequent transition to the labour market. \n \nThe objectives of this project are to determine if there are significant psychological outcome differences in self-concept, self-efficacy, hope, procrastination, and study skills/work ethic between students who pursue WIL and students who pursue a more traditional degree programme. \n \nMethodology \n \nUsing a cross sectional analysis of a large sample of undergraduate students at the University of Huddersfield. Undergraduates in all years of study, pursuing both traditional and WIL degrees, from all schools, will be invited to participate in the study. Demographic information including information regarding educational attainment, work related activity and subject area will be collected. In addition the following measures will be used: \n \n•\tTrait Hope Scale (THS: Snyder et al., 1991), which measures hopes and goals. \n•\tProcrastination Assessment Scale – Students (PASS: Solomon & Rothblum, 1984), which measures procrastination, the postponement of goals and tasks. \n•\tSelf-Description Questionnaire III (SDQ-III: Marsh & O’Neill, 1984), which measures self-concept. \n•\tCollege Academic Self-Efficacy Scale (CASES: Owen & Froman, 1988), which measures the degree of confidence participants believe they have in various academic settings. \n•\tMotivated Strategies for Learning Questionnaire (MSLQ: Pintrich, Smith, Garcia, & McKeachie, 1993), which measures study skills and motivation. \n \nAlthough the results unique to Huddersfield students will be presented in this session, the project itself is part of a much larger international comparative research study, designed and led by Drysdale et al. at the University of Waterloo (Canada) examining psychological outcomes on a global level. Other partners include University West (Sweden), University of Central Florida (USA), and Liverpool John Moores University (UK). Logistics of being involved in a large international project will be discussed. \n \nConclusions \nAs this study is a work-in-progress, results and conclusions will be available and submitted as part of the full paper in August.
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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 | 0.004 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".