Students' reflections of quality (RoQ) in work-integrated learning (WIL): a systematic review and framework
Bibliographic record
Abstract
Purpose In light of the expanding prominence of work-integrated learning (WIL), the pedagogical model that integrates work experiences into an academic curriculum, this paper presents a systematic review that uncovers little-explored students’ reflections of quality (RoQ). Design/methodology/approach Drawing on the concept of wayfinding rocks and Bronfenbrenner’s (1979) ecological systems theory, the “students’ RoQ (pronounced [ROK]) WIL model” offers guidance for future research, policy development and educational interventions aimed at optimizing students' experiences of WIL. Findings This paper highlights RoQ WIL through student voice. The outcomes offer a model, contributing insights for institutions, employers and students involved in WIL experiences. Research limitations/implications While the study addresses specific limitations such as the use of specific search terms and potential biases, future research is needed to explore cultural capital’s influence on WIL quality. A focus on broadening the scope of data collection to include a more comprehensive range of student perspectives is needed. Practical implications The paper suggests practical implications for institutions, employers and educators in designing WIL programs that prioritize student perspectives, ultimately enhancing the quality of WIL experiences. Originality/value By focusing on students' RoQ in WIL, this paper fills a significant gap in the literature and provides a foundation for future research and practice in optimizing WIL engagement and outcomes.
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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 | 0.054 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".