Why the expected change did not happen: frictions between different logics of action when implementing recognition of prior learning for low-qualified workers.

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Title: Why the expected change did not happen: frictions between different logics of action when implementing recognition of prior learning for low-qualified workers.
Authors: Kraus, Katrin1 (AUTHOR) katrin.kraus@ife.uzh.ch
Source: Studies in Continuing Education. Mar2026, Vol. 48 Issue 1, p91-107. 17p.
Subject Terms: *Prior learning, *Vocational education, *Policy sciences, *Semiskilled labor, Labor market, Policy analysis, Social change
Geographic Terms: Switzerland
Abstract: Switzerland sought to reform its VET system by introducing a Recognition of Prior Learning process designed to help low-qualified workers gain formal qualifications. Policy makers believed it would improve labour market opportunities for participants, increase the proportion of the population with post-compulsory education, and alleviate the shortage of skilled workers. Unfortunately, participation levels have been significantly below expectations (Luomi-Messerer 2024; Maurer 2019). It is important to decipher the structural reasons behind these numbers rather than assuming low-qualified workers failed to take advantage of a process designed to help them improve their situation (Cort, Mariager-Anderson, and Thomsen 2018). This paper applies policy mobilities and assemblage theory (PMAT) to analyse the gap between political intentions and actual utilisation, as suggested by Lewis and Spratt (2024). The analysis reveals that while RPL is highly mobile, the characteristics that enhance its mobility are precisely those that generate friction with the institutional logic of the Swiss VET system and hinder institutional change. The assemblage analysis reconstructs the friction points between the two elements and their logics of action more precisely, highlighting why RPL implementation has not fulfilled expectations. [ABSTRACT FROM AUTHOR]
Copyright of Studies in Continuing Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Studies+in+Continuing+Education%22">Studies in Continuing Education</searchLink>. Mar2026, Vol. 48 Issue 1, p91-107. 17p.
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  Data: *<searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Vocational+education%22">Vocational education</searchLink><br />*<searchLink fieldCode="DE" term="%22Policy+sciences%22">Policy sciences</searchLink><br />*<searchLink fieldCode="DE" term="%22Semiskilled+labor%22">Semiskilled labor</searchLink><br /><searchLink fieldCode="DE" term="%22Labor+market%22">Labor market</searchLink><br /><searchLink fieldCode="DE" term="%22Policy+analysis%22">Policy analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Social+change%22">Social change</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Switzerland%22">Switzerland</searchLink>
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  Data: Switzerland sought to reform its VET system by introducing a Recognition of Prior Learning process designed to help low-qualified workers gain formal qualifications. Policy makers believed it would improve labour market opportunities for participants, increase the proportion of the population with post-compulsory education, and alleviate the shortage of skilled workers. Unfortunately, participation levels have been significantly below expectations (Luomi-Messerer 2024; Maurer 2019). It is important to decipher the structural reasons behind these numbers rather than assuming low-qualified workers failed to take advantage of a process designed to help them improve their situation (Cort, Mariager-Anderson, and Thomsen 2018). This paper applies policy mobilities and assemblage theory (PMAT) to analyse the gap between political intentions and actual utilisation, as suggested by Lewis and Spratt (2024). The analysis reveals that while RPL is highly mobile, the characteristics that enhance its mobility are precisely those that generate friction with the institutional logic of the Swiss VET system and hinder institutional change. The assemblage analysis reconstructs the friction points between the two elements and their logics of action more precisely, highlighting why RPL implementation has not fulfilled expectations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Studies in Continuing Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/0158037X.2025.2515144
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      – Code: eng
        Text: English
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        PageCount: 17
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      – SubjectFull: Prior learning
        Type: general
      – SubjectFull: Vocational education
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      – SubjectFull: Policy sciences
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      – SubjectFull: Semiskilled labor
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      – SubjectFull: Labor market
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      – SubjectFull: Policy analysis
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      – SubjectFull: Social change
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      – SubjectFull: Switzerland
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      – TitleFull: Why the expected change did not happen: frictions between different logics of action when implementing recognition of prior learning for low-qualified workers.
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              M: 03
              Text: Mar2026
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              Y: 2026
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