Risks in Work-Integrated Learning: A Data-Driven Analysis

This study employs advanced data-driven and machine learning techniques to critically assess the integration of Work-Integrated Learning (WIL) into academic programs, with a focus on psychological well-being, financial, and equity and inclusion risks. Using data from the 2018 National Graduates Surv...

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Main Author: Xiao Xu
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Education Sciences
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Online Access:https://www.mdpi.com/2227-7102/15/1/106
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author Xiao Xu
author_facet Xiao Xu
author_sort Xiao Xu
collection DOAJ
description This study employs advanced data-driven and machine learning techniques to critically assess the integration of Work-Integrated Learning (WIL) into academic programs, with a focus on psychological well-being, financial, and equity and inclusion risks. Using data from the 2018 National Graduates Survey in Canada, the analysis examines how WIL programs influence students’ academic and career trajectories, with particular emphasis on identifying key risk factors. The study explores psychological well-being risks associated with academic programs, financial burdens both during and after education, and equity and inclusion risks for institutions. By analysing variables related to work placements, student loans, financial assistance, and the alignment of WIL experiences with students’ post-graduation employment, this research provides critical insights into the effectiveness of WIL programs from a large-scale, survey-based, big data perspective. The findings highlight key areas for improvement to mitigate these risks and enhance the overall value of WIL for students across various disciplines.
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spelling doaj-art-876650e14b8740f887e159cffd4a2bb52025-01-24T13:30:37ZengMDPI AGEducation Sciences2227-71022025-01-0115110610.3390/educsci15010106Risks in Work-Integrated Learning: A Data-Driven AnalysisXiao Xu0School of Risk and Actuarial Studies, University of New South Wales, Sydney, NSW 2052, AustraliaThis study employs advanced data-driven and machine learning techniques to critically assess the integration of Work-Integrated Learning (WIL) into academic programs, with a focus on psychological well-being, financial, and equity and inclusion risks. Using data from the 2018 National Graduates Survey in Canada, the analysis examines how WIL programs influence students’ academic and career trajectories, with particular emphasis on identifying key risk factors. The study explores psychological well-being risks associated with academic programs, financial burdens both during and after education, and equity and inclusion risks for institutions. By analysing variables related to work placements, student loans, financial assistance, and the alignment of WIL experiences with students’ post-graduation employment, this research provides critical insights into the effectiveness of WIL programs from a large-scale, survey-based, big data perspective. The findings highlight key areas for improvement to mitigate these risks and enhance the overall value of WIL for students across various disciplines.https://www.mdpi.com/2227-7102/15/1/106work-integrated learningfinancial riskequity and inclusion riskwell-being risk
spellingShingle Xiao Xu
Risks in Work-Integrated Learning: A Data-Driven Analysis
Education Sciences
work-integrated learning
financial risk
equity and inclusion risk
well-being risk
title Risks in Work-Integrated Learning: A Data-Driven Analysis
title_full Risks in Work-Integrated Learning: A Data-Driven Analysis
title_fullStr Risks in Work-Integrated Learning: A Data-Driven Analysis
title_full_unstemmed Risks in Work-Integrated Learning: A Data-Driven Analysis
title_short Risks in Work-Integrated Learning: A Data-Driven Analysis
title_sort risks in work integrated learning a data driven analysis
topic work-integrated learning
financial risk
equity and inclusion risk
well-being risk
url https://www.mdpi.com/2227-7102/15/1/106
work_keys_str_mv AT xiaoxu risksinworkintegratedlearningadatadrivenanalysis