A Hybrid Multi-recommender System for a Teaching and Learning Community for the Dual System of Vocational Education and Training

  • Xi Kong
  • Susanne Boll
  • Wilko Heuten
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8095)


A number of recommender systems (RS) have been or are being developed in the context of technology enhanced learning (TEL). However, there seems to be a lack of research focusing on the dual system of vocational education and training (VET). The knowledge transfer and sharing in a dual system has its own particularities and difficulties because of its inborn nature. Firstly, in the dual system, apprentices in-company practice training is at the workplace and their theoretical education is in the classroom in a vocational school. The transfer of know-how takes place in two different geo-location. Secondly, different stakeholders are involved in the dual system in the sense of knowledge transfer and sharing. We consider three roles for analyzing the knowledge transfer and sharing, i.e. trainee, trainer and teacher. This suggests, six flows of knowledge transfer and sharing may occur, i.e. flow: teacher ⇔ trainee, trainer ⇔ trainee, teacher ⇔ trainer, trainee ⇔ trainee, trainer ⇔ trainer, teacher ⇔ teacher. Knowledge transfer and sharing among trainee, teacher and trainer is not easy, if there is no fitting instrument to support this process. The project expertAzubi addresses these issues to develop an online teaching and learning community using Web 2.0 technologies. We propose a hybrid multi-recommender approach to (1) support knowledge transfer and sharing among trainees, teachers and trainers in a pro-active way, and (2) use particularly the user generated contents to generate personalized recommendations to motivate learners to initialize an active lifelong learning.


  1. 1.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)CrossRefGoogle Scholar
  2. 2.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender systems in technology enhanced learning. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook. Springer (2011)Google Scholar
  3. 3.
    Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xi Kong
    • 1
  • Susanne Boll
    • 2
  • Wilko Heuten
    • 1
  1. 1.Institute for Information TechnologyOFFISOldenburgGermany
  2. 2.Media Informatics and Multimedia SystemUniversity of OldenburgGermany

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