Skip to main content

An Ontology-Based and Case-Based Reasoning Supported Workplace Learning Approach

  • Conference paper
  • First Online:
Model-Driven Engineering and Software Development (MODELSWARD 2016)

Abstract

The support of workplace learning is increasingly relevant as the change in every form determines today’s working world in the industry and public administrations alike. Adapting quickly to a new job, a new task or a new team is a significant challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it is aligned with business goals. Our approach supports workplace learning by suggesting historical cases and providing recommendations of experts and learning resources. We utilize users’ workplace environment, we consider their learning preferences, provide them with useful prior lessons, and compare required and acquired competencies to issue the best-suited recommendations. Our research work follows a Design Science Research strategy and is part of the European funded project Learn PAd. The recommender system introduced here is evaluated in an iterative manner, first by comparing it to previously elicited user requirements and then through practical application in a test process conducted by the project application partner.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. US Bureau of Labor Statistics: Employee Tenure in 2014. Washington, USA (2014) http://www.bls.gov/news.release/pdf/tenure.pdf

  2. Meister, J.: Job Hopping Is the ‘New Normal’ for Millennials: Three Ways to Prevent a Human Resource Nightmare

    Google Scholar 

  3. Accenture: Career Capital 2014 Global Research Results (2014)

    Google Scholar 

  4. Tynjälä, P.: Perspectives into learning at the workplace. Educ. Res. Rev. 3, 130–154 (2008)

    Article  Google Scholar 

  5. De Angelis, G., Pierantonio, A., Polini, A., Re, B., Thönssen, B., Woitsch, R.: Modelling for Learning in Public Administrations – The Learn PAd approach. In: Karagiannis D., Mayr H., Mylopoulos J. (eds.) Domain-Specific Conceptual Modelling: Concepts, Methods, and Tools. Springer, Cham (2015)

    Google Scholar 

  6. OMG: Business Process Model and Notation (BPMN) Version 2.0. Object Management Group (2013) http://www.omg.org/spec/BPMN/2.0.2/

  7. OMG: Business Motivation Model (BMM) Version 1.2. Object Management Group (2014) http://www.omg.org/spec/BMM/1.2

  8. European Comission: Descriptors defining levels in the European Qualifications Framework (EQF) (2016) https://ec.europa.eu/ploteus/content/descriptors-page

  9. Hevner, A., Chatterjee, S.: Design Research in Information Systems. Springer, Boston (2010)

    Google Scholar 

  10. Grüninger, M., Fox, M.S.: Methodology for the design and evaluation of ontologies. In: Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-1995, Montreal, pp. 1–10 (1995)

    Google Scholar 

  11. Ghauth, K.I., Abdullah, N.A.: The Effect of incorporating good learners’ ratings in e-learning content-based recommender system. Educ. Technol. Soc. 14, 248–257 (2010)

    Google Scholar 

  12. Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Educ. Technol. Soc. 12, 30–42 (2009)

    Google Scholar 

  13. Zaíane, O.R.: Building a recommender agent for e-learning systems. In: Proceedings of the International Conference on Computers in Education, p. 55. IEEE Computer Society (2002)

    Google Scholar 

  14. Sikka, R., Dhankhar, A., Rana, C.: A survey paper on e-learning recommender system. Int. J. Comput. Appl. 47, 27–30 (2012)

    Google Scholar 

  15. Schmidt, A., Winterhalter, C.: User context aware delivery of e-learning material: Approach and architecture. J. Univers. Comput. Sci. 10, 28–36 (2004)

    Google Scholar 

  16. Yu, Z., Nakamura, Y., Jang, S., Kajita, S., Mase, K.: Ontology-Based Semantic Recommendation for Context-Aware E-Learning. In: Indulska, J., Ma, J., Yang, Laurence T., Ungerer, T., Cao, J. (eds.) UIC 2007. LNCS, vol. 4611, pp. 898–907. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73549-6_88

    Chapter  Google Scholar 

  17. Abecker, A., Bernardi, A., Hinkelmann, K., Kühn, O., Sintek, M.: Toward a well-founded technology for organizational memories. IEEE Intell. Syst. Appl. 13, 40–48 (1998)

    Article  Google Scholar 

  18. Abecker, A., Bernardi, A., Hinkelmann, K., Ku¨hn, O., Sintek, M.: Context-aware, proactive delivery of task-specific information: the knowmore project. Inf. Syst. Front. 2, 253–276 (2000)

    Article  Google Scholar 

  19. Rychen, D.S., Salganik, L.H.: Key competencies for a successful life and a well-functioning society. OECD Defin. Sel. Competencies Final report. 1–20 (2003)

    Google Scholar 

  20. Ferrari, A.: DIGCOMP: A Framework For Developing And Understanding Digital Competence in Europe. (2013)

    Google Scholar 

  21. European e-Competence Framework (e-CF), Version 3.0 (2016) http://www.ecompetences.eu/

  22. Forehand, M.: Bloom’s Taxonomy. Emerg. Perspect. Learn. Teaching Technol. 12 (2012)

    Google Scholar 

  23. Vygotsky, L.S.: Mind in Society: The Development of Higher Psychological Processes, p. 159. Harvard university press, Cambridge (1978)

    Google Scholar 

  24. Wood, D., Bruner, J.S., Ross, G.: The role of tutoring in problem solving. J. Child Psychol. Psychiatry 17, 89–100 (1976)

    Article  Google Scholar 

  25. Kim, M.C., Hannafin, M.J.: Scaffolding problem solving in technology-enhanced learning environments (TELEs): bridging research and theory with practice. Comput. Educ. 56, 403–417 (2011)

    Article  Google Scholar 

  26. Quintana, C., Reiser, B.J., Davis, E.A., Krajcik, J., Fretz, E., Duncan, R.G.: A scaffolding design framework for software to support science inquiry. J. Learn. Sci. 13, 37–41 (2004)

    Article  Google Scholar 

  27. Boud, D.: Current Issues and New Agendas in Workplace Learning, p. 163. National Centre for Vocational Education Research, Leabrook (1998)

    Google Scholar 

  28. Siemens, G.: Connectivism: a learning theory for the digital age. Int. J. Instr. Technol. Distance Learn. 2, 3–10 (2005)

    Google Scholar 

  29. Dunn, R., Dunn, K.J.: Teaching Students Through Their Individual Learning Styles: A Practical Approach. Reston Pub. Co., Reston (1978)

    Google Scholar 

  30. Leake, D.B.: CBR in context: the present and future. In: Leake, D.B. (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions. pp. 1–35. AAAI Press/MIT Press, Menlo Park (1996)

    Google Scholar 

  31. Watson, I.: Case-based reasoning is a methodology not a technology. Knowl.-Based Syst. 12, 303–308 (1999)

    Article  MathSciNet  Google Scholar 

  32. Kolodner, J.L.: Case-based reasoning. Morgan Kaufmann Publishers, San Mateo (1993)

    Book  MATH  Google Scholar 

  33. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7, 39–59 (1994)

    Google Scholar 

  34. Bergmann, R.: Experience Management: Foundations, Development Methodology, and Internet-Based Applications. Springer, Berlin Heidelberg, Berlin, Heidelberg (2002)

    Book  MATH  Google Scholar 

  35. Martin, A., Emmenegger, S., Hinkelmann, K., Thönssen, B.: A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management. Enterp. Inf. Syst. 11, 1–25 (2016)

    Google Scholar 

  36. Bergmann, R., Althoff, K.-D., Breen, S., Göker, M., Manago, M., Traphöner, R., Wess, S.: Developing Industrial Case-Based Reasoning Applications. Springer, Berlin Heidelberg, Berlin, Heidelberg (2003)

    Book  MATH  Google Scholar 

  37. Díaz-Agudo, B., González-Calero, P.A.: An Architecture for Knowledge Intensive CBR Systems. In: Blanzieri, E., Portinale, L. (eds.) Advances in Case-Based Reasoning, pp. 37–48. Springer, Berlin / Heidelberg, Berlin, Heidelberg (2000)

    Chapter  Google Scholar 

  38. Recio-Garía, J.A., Díaz-Agudo, B.: Ontology based CBR with jCOLIBRI. In: Ellis, R., Allen, T., and Tuson, A. (eds.) Proceedings of AI-2006, The Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 149–162. Springer, London (2007)

    Google Scholar 

  39. Gao, J., Deng, G.: Semi-automatic construction of ontology-based cbr system for knowledge integration. Int. J. Electr. Electron. Eng. 4, 297–303 (2010)

    Google Scholar 

  40. Martin, A., Emmenegger, S., Wilke, G.: Integrating an enterprise architecture ontology in a case-based reasoning approach for project knowledge. In: Proceedings of the First International Conference on Enterprise Systems: ES 2013. pp. 1–12. IEEE, Cape Town (2013)

    Google Scholar 

  41. Recio-García, J.A., Díaz-Agudo, B., González-Calero, P.: jCOLIBRI2 Tutorial, Group of Artificial Intelligence Application (GAIA). University Complutense of Madrid. Document Version 1.2 (2008)

    Google Scholar 

  42. Kang, D., Lee, J., Choi, S., Kim, K.: An ontology-based enterprise architecture. Expert Syst. Appl. 37, 1456–1464 (2010)

    Article  Google Scholar 

  43. Feldkamp, D., Hinkelmann, K., Thönssen, B.: KISS – Knowledge-Intensive Service Support: An Approach for Agile Process Management. In: Paschke, A., Biletskiy, Y. (eds.) RuleML 2007. LNCS, vol. 4824, pp. 25–38. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75975-1_3

    Chapter  Google Scholar 

  44. Thönssen, B.: An Enterprise Ontology Building the Bases for Automatic Metadata Generation. In: Sánchez-Alonso, S., Athanasiadis, Ioannis N. (eds.) MTSR 2010. CCIS, vol. 108, pp. 195–210. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16552-8_19

    Chapter  Google Scholar 

  45. Lankhorst, M.: Enterprise Architecture at Work. Springer, Heidelberg (2009)

    Google Scholar 

  46. Bello-Tomás, J.J., González-Calero, P.A., Díaz-Agudo, B.: JColibri: An object-oriented framework for building CBR systems. In: Funk, P., González Calero, P. (eds.) Advances in Case-Based Reasoning SE - 4, pp. 32–46. Springer, Berlin Heidelberg (2004)

    Chapter  Google Scholar 

  47. Roth-Berghofer, T., Bahls, D.: Explanation capabilities of the open source case-based reasoning tool myCBR. In: Petridis, M. and Wiratunga, N. (eds.) UK Workshop on Case-Based Reasoning UKCBR 2008, pp. 23–34 (2008)

    Google Scholar 

  48. Assali, A.A., Lenne, D., Debray, B.: Heterogeneity in Ontological CBR Systems. In: Montani, S., Jain, L. (eds.) Successful Case-based Reasoning Applications-I SE-5, pp. 97–116. Springer, Berlin Heidelberg (2010)

    Chapter  Google Scholar 

  49. Dıaz-Agudo, B., González-Calero, P.: Knowledge intensive CBR made affordable. In: Weber, R., Gresse von Wangenheim, C. (eds.) Proceedings of the Workshop Program at the Fourth International Conference on Case-Based Reasoning. Navy Center for Applied Research in Artificial Intelligence Washington, DC, USA (2001)

    Google Scholar 

  50. Wang, Y., Hu, T., Zhang, S.: Ontology-based reconfigurable case-based reasoning system for knowledge integration. In: SMC 2003 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), pp. 4878–4883. IEEE, New York(2003)

    Google Scholar 

  51. Witschel, H.F., Martin, A., Emmenegger, S., Lutz, J.: A new retrieval function for ontology-based complex case descriptions. In: International Workshop Case-Based Reasoning CBR-MD 2015. ibai-publishing, Hamburg (2015)

    Google Scholar 

  52. Cohen, W.W., Ravikumar, P.D., Fienberg, S.E.: A comparison of string distance metrics for name-matching tasks. In: Kambhampati, S., Knoblock, C.A. (eds.) Proceedings of IJCAI-03 Workshop on Information Integration on the Web, Acapulco, Mexico, pp. 73–78 (2003)

    Google Scholar 

  53. Bechhofer, S., Harmelen, F., Hendler, J., Horrocks, I., McGuiness, D.L., Patel-Schneider, P.F., Stein, L.A.: OWL Web Ontology Language Reference

    Google Scholar 

  54. Dickinson, I.: Jena Ontology API (2009) http://jena.sourceforge.net/ontology/

  55. Eclipse Foundation: Eclipse Modeling Framework (EMF) (2016) https://eclipse.org/modeling/emf/

  56. W3C: XSL Transformations (XSLT) Version 1.0, World Wide Web Consortium (1999) https://www.w3.org/TR/xslt

  57. W3C: RDF Schema 1.1, World Wide Web Consortium (2014) http://www.w3.org/TR/rdf-schema/

  58. W3C: SPIN SPARQL Inferencing Notation, World Wide Web Consortium (2011) http://spinrdf.org/

  59. Fanesi, D.: A Multilayer ontology to represent business process models and execution data, Master’s thesis, University of Applied Sciences and Arts Northwestern Switzerland and University of Camerino (2015)

    Google Scholar 

  60. Fanesi, D., Cacciagrano, D.R., Hinkelmann, K.: Semantic business process representation to enhance the degree of BPM mechanization-an ontology. In: ES2015 Conference Proceedings, International Conference on Enterprise Systems, pp. 21–32. IEEE (2015)

    Google Scholar 

Download references

Acknowledgements

This work is supported by the European Union FP7 ICT objective, through the Learn PAd Project with Contract No. 619583.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emanuele Laurenzi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Emmenegger, S. et al. (2017). An Ontology-Based and Case-Based Reasoning Supported Workplace Learning Approach. In: Hammoudi, S., Pires, L., Selic, B., Desfray, P. (eds) Model-Driven Engineering and Software Development. MODELSWARD 2016. Communications in Computer and Information Science, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-319-66302-9_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66302-9_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66301-2

  • Online ISBN: 978-3-319-66302-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics