Work integrated learning and trending areas for future studies: a systematic literature review and bibliometric analysis
Bibliographic record
Abstract
Purpose This study intends to conceptually and technically examine the literature on work integrated learning (WIL) through a systematic literature review and bibliometric analysis. The present study addresses eight distinct research questions: (1) descriptive features of the extracted literature on WIL, (2) publications trends and thematic evolution in the field of WIL, (3) the most relevant and high-impact sources on WIL, (4) the most global cited articles on WIL, (5) the most relevant and high-impact authors on WIL, (6) the most relevant countries on WIL, (7) outcomes of Bradford’s Law of Scattering and Lotka’s Law of Scientific Productivity and (8) trending research avenues for future studies in the field of WIL. Design/methodology/approach The present study employed systematic literature review (SLR) and bibliometric analysis mapping techniques to analyze 1,295 articles extracted from the Scopus database. The analysis utilized Biblioshiny software and VOSviewer software as the primary tools. Findings The findings reveal that WIL constitutes a steadily expanding subject discipline, showcasing a notable 23.28% annual growth in scientific production spanning from 2002 to 2023 (July). Australia, South Africa and Canada emerged as the most productive countries within the field of WIL, as evidenced by their cumulative scientific production. The thematic map of keyword analysis suggests several burgeoning avenues for future researchers in the WIL domain, including education, reflective practices, curriculum, employability skills, international students, learning and self-efficacy. Originality/value This study contributes to the WIL discourse by providing a comprehensive literature review. The present study’s findings hold significance for graduates, universities, employers, the higher education industry, policymakers, regulators and the broader community.
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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 | 0.052 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.153 | 0.108 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".