Early detection of dropout factors in vocational education: A large-scale case study from Finland

Abstract

The aim of this study is to analyze which factors from students' admission data can predict dropout in initial vocational education and training (VET) in Finland. The sample included 15, 523 students in different fields of VET that started an initial VET between 2014 and 2021 in a large-size vocational school in Finland. The results of fitting a logistic regression model to the admission data showed that students who started a VET program after basic education were more likely to drop out, as well as students who combined their studies with a job or job-seeking. Our findings pave the pathway for further research to implement support measures for decreasing dropout that are tailored to each specific “risk group”. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Keywords

Author Keywords

Dropout vocational education and training (VET) Learning Analytics prediction

Index Keywords

Case-studies Risk assessment Apprentices Education computing Students Vocational education Large-scales Vocational education and training Vocational schools Finland Logistic regression Learning analytic Logistic Regression modeling Dropout

Author Affiliations

School of Computing, Itä-Suomen yliopisto, Kuopio, IS, Finland, School of Applied Educational Science and Teacher Education, Itä-Suomen yliopisto, Kuopio, IS, Finland

School of Applied Educational Science and Teacher Education, Itä-Suomen yliopisto, Kuopio, IS, Finland

Funding & Acknowledgements

European Regional Development Fund, ERDF

Grant: 5145/31/2019

This article was supported by funding from Business Finland through the European Regional Development Fund (ERDF) project “Utilization of learning analytics in the various educational levels for supporting self-regulated learning (OAHOT)” (Grant No. 5145/31/2019).

Business Finland

This article was supported by funding from Business Finland through the European Regional Development Fund (ERDF) project “Utilization of learning analytics in the various educational levels for supporting self-regulated learning (OAHOT)” (Grant No. 5145/31/2019).

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Early detection of dropout factors in vocational education: A large-scale case study from Finland

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