Article Gold Open Access 2024

Learning outcome evaluation in manual assembly

Production Engineering
Journal · Vol. 18 · Issue 6 · pp. 941-953
Abstract

Mass customization and shorter product life cycles are causing ever more variants in production, especially in manual assembly. At the same time, more diverse personnel structures are emerging due to demographic change and labor shortages. This is causing different challenges to production managers, e.g., competence gaps. To meet these challenges, learning in manual assembly becomes increasingly important. The design of the learning process can only be improved by checking whether the processes fulfill their purpose. Various learning evaluation measures are described in general vocational education and competence development, but it is hard to select the right one for the learning process. This paper shows a procedure, how learning evaluation measures can be selected, and how they can measure learning progress. For this, a test person study was conducted to compare different learning evaluation measures and show their usability and advantages in manual assembly. The results support making learning in assembly easier to apply and controllable. In the long term, feeding back the results improves the learning process design. © The Author(s) 2024.

Keywords

Author Keywords

work-based learning competence development Manufacturing Learning outcome Learning Evaluation Assembly worker

Index Keywords

Learning systems Learning outcome Work-Based Learning Assembly Competence development manufacturing Learning process Life cycle Assembly workers Evaluation measures Learning evaluations Manual assembly Outcome evaluation
Author Affiliations
Technische Universität München, Munich, Bayern, Germany
Funding & Acknowledgements
Bundesministerium für Wirtschaft und Klimaschutz, BMWK
Grant: 01MF22002B
The authors thank the German Federal Ministry for Economic Affairs and Climate Action (BMWK) for its financial and organizational support of the \u2018Mittelstand-Digital Zentrum Augsburg\u2019 [Grant no. 01MF22002B].
References 10 References
1 Industry 5 0 Towards A Sustainable Human Centric and Resilient European Industry, (2021)
2 J Appl Leadersh Manag, (2020)
3 Uncertain Framework Conditions Slow Down German Economy Dihk Economic Survey Fall, (2023)
4 Build Learning into Your Employees Workflow, (2022)
5 Burggräf, Peter, Adaptive assembly systems for enabling agile assembly - Empirical analysis focusing on cognitive worker assistance, Procedia CIRP, 97, pp. 319-324, (2020)
6 Maier, Maria, Concept for the Competence Development and Learning Process of Assembly Workers, 2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023, pp. 1483-1487, (2023)
7 European Guidelines for Validating Non Formal and Informal Learning, (2023)
8 Sehr, Philip, Am I Done Learning? - Determining Learning States in Adaptive Assembly Systems, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2022-September, (2022)
9 Knowledge Management Value Creation Through Organizational Learning, (2018)
10 Handbuch Kompetenzmessung Erkennen Verstehen Und Bewerten Von Kompetenzen in Der Betrieblichen Padagogischen Und Psychologischen Praxis, (2017)
Quick Actions
Full Text via DOI
Citation Metrics
0
Times Cited (Scopus)

References 10
Document Identifiers