What WIL We Do? Improving the Practice of Student Preparation Prior to Work-Integrated Learning Experiences Through Innovative Curriculum Design
Bibliographic record
Abstract
Globalization, the rise of the digital economy, and the restrictions caused by the COVID-19 pandemic (Dean & Campbell, 2020; Sutherland & Symmons, 2013; Zegwaard et al., 2020) have contributed to the emergence of several modalities of work-integrated learning (WIL) in higher education. The result of all this activity has been a rise in the need to discuss WIL curriculum design, particularly as it pertains to student preparation before a WIL experience (Cooper et al., 2010). Further research in this domain is necessary to address employer perceived deficiencies in student performance and ability upon entering the workplace for WIL (Jackson et al., 2017; Winterton & Turner, 2019). In this paper, I analyze and interpret the theoretical underpinnings of WIL curriculum design and align these tenets with emerging pedagogical approaches that reinforce the desired learning outcomes for WIL while also addressing a skills gap. I draw on the recent WIL literature on the theories of scaffolding, critical reflection, transformative learning, and agency, and showcase how methods such as problem-based learning, digital game-based learning, the co-operative learning method, and case-based learning have the potential to enhance student preparation before WIL. The discourse in this paper has an aim of highlighting opportunities to expand the literature for WIL curriculum design to improve pedagogical outcomes in higher education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".