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Learn but work!: towards self-directed learning at mobile assembly workplaces

Published:21 October 2015Publication History

ABSTRACT

In comparison to other work domains and to the current state of research, we face on the manufacturing shop floor little attention for the role of informal learning as key driver for manual assembly processes. There, smart assistance systems focus on process qualities and outcomes, not on the workers' abilities to cope with novel or uncertain work situations. In this paper we present a conceptual approach on how to integrate cognitive automation and self-directed learning at the assembly workplace. We argue for embedding smart learning behavior, based on the model of cognitive apprenticeship and provided by a cognitive architecture, with existing cognitive automation approaches. The conceptual approach is illustrated with the Plant@Hand smart assembly assistant prototype for the industrial assembly workplace.

References

  1. M. Aehnelt and S. Bader. Information assistance for smart assembly stations. In S. Loiseau, J. Filipe, B. Duval, and van den Herik, Jaap, editors, Proceedings of the 7th International Conference on Agents and Artificial Intelligence (ICAART 2015), volume 2, pages 143--150, Lisbon, Portugal, 2015. SciTePress.Google ScholarGoogle Scholar
  2. M. Aehnelt and B. Urban. The knowledge gap: providing situation-aware information assistance on the shop floor. In Proceedings of the 17th International Conference on Human-Computer Interaction: 2-7 August 2015, Los Angeles, USA. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Collins, J. S. Brown, and S. E. Newman. Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. Knowing, learning, and instruction: Essays in honor of Robert Glaser, 18:32--42, 1989.Google ScholarGoogle Scholar
  4. B. W. Crandall and R. R. Hoffman. Cognitive task analysis. In J. D. Lee and A. Kirlik, editors, The Oxford handbook of cognitive engineering, Oxford library of psychology, pages 229--239. Oxford University Press, 2013.Google ScholarGoogle Scholar
  5. G. Dohmen. Das informelle Lernen: Die internationale Erschließung einer bisher vernachlässigten Grundform menschlichen Lernens für das lebenslange Lernen aller. Bundesministerium für Bildung und Forschung, BMBF, 2001.Google ScholarGoogle Scholar
  6. M. Eraut. Informal learning in the workplace: evidence on the real value of work-based learning (wbl). Development and Learning in Organizations, 25(5):8--12, 2011.Google ScholarGoogle Scholar
  7. Å. Fast-Berglund, T. Fässberg, F. Hellman, A. Davidsson, and J. Stahre. Relations between complexity, quality and cognitive automation in mixed-model assembly. Journal of manufacturing systems, 32(3):449--455, 2013.Google ScholarGoogle Scholar
  8. M. Kerres. Mediendidaktik: Konzeption und Entwicklung mediengestützter Lernangebote. Walter de Gruyter, 2013.Google ScholarGoogle Scholar
  9. R. Koper. Conditions for effective smart learning environments. Smart Learning Environments, 1(1):1--17, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  10. O. Korn, M. Funk, and A. Schmidt. Assistive systems for the workplace. In L. B. Theng, editor, Assistive Technologies for Physical and Cognitive Disabilities, pages 121--135. IGI Global, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  11. O. Korn, A. Schmidt, and T. Hörz. Assistive systems in production environments: exploring motion recognition and gamification. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, pages 1--5. ACM, Heraklion, Crete, Greece, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. B. Krogstie, M. Prilla, and V. Pammer. Understanding and supporting reflective learning processes in the workplace: The csrl model. In D. Hernández-Leo, T. Ley, R. Klamma, and A. Harrer, editors, Scaling up Learning for Sustained Impact, volume 8095 of Lecture Notes in Computer Science, pages 151--164. Springer Berlin Heidelberg, 2013.Google ScholarGoogle Scholar
  13. T. Ley, J. Cook, S. Dennerlein, M. Kravcik, C. Kunzmann, M. Laanpere, K. Pata, J. Purma, J. Sandars, P. Santos, and A. Schmidt. Scaling informal learning: An integrative systems view on scaffolding at the workplace. In D. Hernández-Leo, T. Ley, R. Klamma, and A. Harrer, editors, Scaling up Learning for Sustained Impact, volume 8095 of Lecture Notes in Computer Science, pages 484--489. Springer Berlin Heidelberg, 2013.Google ScholarGoogle Scholar
  14. S. N. Lindstaedt, M. Aehnelt, and R. d. Hoog. Supporting the learning dimension of knowledge work. In U. Cress, V. Dimitrova, and M. Specht, editors, Learning in the synergy of multiple disciplines, volume 5794 of Lecture Notes in Computer Science, pages 639--644. Springer, Berlin {u.a.}, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Mavrikios, N. Papakostas, D. Mourtzis, and G. Chryssolouris. On industrial learning and training for the factories of the future: a conceptual, cognitive and technology framework. Journal of Intelligent Manufacturing, 24(3):473--485, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. E. M. Roth and A. M. Bisantz. Cognitive work analysis. In J. D. Lee and A. Kirlik, editors, The Oxford handbook of cognitive engineering, Oxford library of psychology, pages 240--260. Oxford University Press, 2013.Google ScholarGoogle Scholar
  17. U. Schiefele and I. Schreyer. Intrinsische lernmotivation und lernen. ein überblick zu ergebnissen der forschung. Zeitschrift für pädagogische Psychologie, 8(1):1--13, 1994.Google ScholarGoogle Scholar
  18. D. A. Thurman, D. M. Brann, and C. M. Mitchell. An architecture to support incremental automation of complex systems. In Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on, volume 2, pages 1174--1179, 1997.Google ScholarGoogle Scholar
  19. K. J. Vicente. Cognitive work analysis: Toward safe, productive, and healthy computer-based work. CRC Press, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. K. E. Watkins and V. J. Marsick. Towards a theory of informal and incidental learning in organizations?. International journal of lifelong education, 11(4):287--300, 1992.Google ScholarGoogle Scholar
  21. E. Wenger and J. Lave. Situated Learning: Legitimate Peripheral Participation (Learning in Doing: Social, Cognitive and Computational Perspectives) by. Cambridge University Press, Cambridge, UK, 1991.Google ScholarGoogle Scholar
  22. F. Wild, P. Scott, J. Karjalainen, K. Helin, S. Lind-Kohvakka, and A. Naeve. An augmented reality job performance aid for kinaesthetic learning in manufacturing work places. In C. Rensing, S. d. Freitas, T. Ley, and P. Muñoz-Merino, editors, Open Learning and Teaching in Educational Communities, volume 8719 of Lecture Notes in Computer Science, pages 470--475. Springer International Publishing, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        • Published in

          cover image ACM Other conferences
          i-KNOW '15: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business
          October 2015
          314 pages
          ISBN:9781450337212
          DOI:10.1145/2809563
          • General Chairs:
          • Stefanie Lindstaedt,
          • Tobias Ley,
          • Harald Sack

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          New York, NY, United States

          Publication History

          • Published: 21 October 2015

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          i-KNOW '15 Paper Acceptance Rate25of78submissions,32%Overall Acceptance Rate77of238submissions,32%

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