Advertisement

Learning and Performance Support - Personalization Through Personal Assistant Technology

  • Jean-Francois LapointeEmail author
  • Heather Molyneaux
  • Irina Kondratova
  • Aida Freixanet Viejo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9753)

Abstract

Personalization is important for online learning due to the ever changing needs of online learners and because of its potential to reach a wide variety of users. This paper describes the results of a literature review about the personalization of online learning systems. It also describes results of user studies of the prototype of a learning and performance support (LPSS) platform developed at the National Research Council of Canada. Main findings are that personalized learning systems can enhance learning effectiveness and motivate learners, and that learners are looking for ways to better explore their learning context through social network.

Keywords

Personal learning environment Personalization Collaborative learning Learning and performance support 

References

  1. 1.
    Swartzberg, C., Swartzberg, R.: Digital Education Gets Smart with Personalized Learning Paths. Bizcommunity. Education and training (2015)Google Scholar
  2. 2.
    Sarrrazin, H., Sprague, K., Huskins, M.: Enabling Enterprise Collaboration. McKinsey & Company, New York (2013)Google Scholar
  3. 3.
    Rosenberg, M.J., Foreman, S.: Learning and Performance Ecosystems: Strategy, Technology, Impact and Challenges. White paper. The eLearning Guild (2014)Google Scholar
  4. 4.
    Siemens, G.: Connectivism: A Learning Theory for the Digital Age. eLearnspace (2004)Google Scholar
  5. 5.
    Dascalu, M., Bodea, C., Moldoveanu, A., Mohora, A., Lytras, M., Pablos, P.O.: A recommender agent based on learning styles for better virtual collaborative learning experiences. Comput. Hum. Behav. 45, 243–253 (2015)CrossRefGoogle Scholar
  6. 6.
    Ali, S.M., Ghani, I., Latiff, M.S.A.: Interaction-based collaborative recommendation: a personalized learning environment (PLE) perspective. KSII Trans. Internet Inf. Syst. 9(1), 446–464 (2015)Google Scholar
  7. 7.
    Downes, S.: LMS vs PLE. YouTube. https://www.youtube.com/watch?v=zDwcCJncyiw (2012)
  8. 8.
    Fournier, H., Molyneaux, H.: Learning and Performance Support Systems: Personal Learning Record: User Studies White Paper. NPARC #: 21275411, p. 19. doi: http://doi.org/10.4224/21275411 (2015)
  9. 9.
    Downes, S.: Design Elements in a Personal Learning Environment. Lecture presentation delivered to Invited Talk, Guadalajara, Mexico (2015)Google Scholar
  10. 10.
    Pane, J.F., Steiner, E.D., Baird, M.D., Hamilton, L.S.: Continued Progress: Promising Evidence on Personalized Learning. RAND Corporation, Santa Monica, CA (2015). http://www.rand.org/pubs/research_reports/RR1365.html
  11. 11.
    Grant, P., Basye, D.: Personalized Learning. International Society for Technology in Education, Arlington (2014)Google Scholar
  12. 12.
    Downes, S.: When U.S. air force discovered the flaw of averages. http://www.downes.ca/post/64908 (2016)
  13. 13.
    Bloom, B.: Learning for Mastery. Evaluation Comment v1 n2. Center for the Study of Evaluation of Instructional Programs, University of California, Los Angeles, USA (1968)Google Scholar
  14. 14.
    Akbari, F., Taghiyareh, F.: E-SoRS: a personalized and social recommender service for e-learning environments. In: 8th National and 5th International Conference on e-Learning and e-Teaching (ICeLeT), Tehran (2014)Google Scholar
  15. 15.
    Chen, W., Su, M., He, X., Chen, Y.: Personalized learning instant support service. J. Convergence Inf. Technol. (JCIT) 8(1), 578–587 (2013)CrossRefGoogle Scholar
  16. 16.
    Dascalu, M.-I., Bodea, C.-N., Moldoveanu, A., Mohora, A., Lytras, M., de Ordóñez Pablos, P.: A recommender agent based on learning styles for better virtual collaborative learning experiences. Comput. Hum. Behav. 45, 243–253 (2015)CrossRefGoogle Scholar
  17. 17.
    Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z.: E-learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56(3), 885–899 (2011)CrossRefGoogle Scholar
  18. 18.
    García-Peñalvo, F.J., Zangrando, V., García Holgado, A., Gónzalez Conde, M.Á., Seone Pardo, A.M., Alier Forment, M., Janssen, J., Griffiths, D., Mykowska, A., Ribeiro Alves, G., Minovic, M.: TRAILER project overview: tagging, recognition and acknowledgment of informal learning experiences. In: 2012 International Symposium on Computers in Education (SIIE). Institute of Electrical and Electronics Engineers, IEEE Catalog Number CFP1286T-ART, Andorra la Vella (2012)Google Scholar
  19. 19.
    Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-aware recommender systems for learning: a survey and future challenge. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)CrossRefGoogle Scholar
  20. 20.
    Chen, C.-M., Lee, H.-M., Chenb, Y.-H.: Personalized e-learning system using item response theory. Comput. Educ. 44(3), 237–255 (2005)CrossRefGoogle Scholar
  21. 21.
    Schiaffino, S., Garcia, P., Amandi, A.: eTeacher: providing personalized assistance to e-learning students. Comput. Educ. 51(4), 1744–1754 (2008)CrossRefGoogle Scholar
  22. 22.
    Kurilovas, E., Kubilinskiene, S., Dagiene, V.: Web 3.0-based personalisation of learning objects in virtual learning environments. Comput. Hum. Behav. 30, 654–662 (2014)CrossRefGoogle Scholar
  23. 23.
    Saadatmand, M., Kumpulainen, K.: Content aggregation and knowledge sharing in a personal learning environment. iJET 8(S1), 70–77 (2013)Google Scholar
  24. 24.
    Shen, L., Wang, M., Shen, R.: Affective e-Learning: using “emotional” data to improve learning in pervasive learning environment. Educ. Technol. Soc. 12(2), 176–189 (2009)Google Scholar
  25. 25.
    Huang, M.-J., Huang, H.-S., Chen, M.-Y.: Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Syst. Appl. 33, 551–564 (2007). Science DirectCrossRefGoogle Scholar
  26. 26.
  27. 27.
    National Research Council of Canada: Social, Technical and Economic Realities Collide: The Perfect Storm for Personal Learning. NRC, p. 13. http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?action=shwart&index=an&req=21275106&lang=en (2015)
  28. 28.
    Höver, K.M, Hartle, M., Rößling, G., Mühlhäuser, M.: Evaluating how students would use a collaborative linked learning space. In: Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education, 27 June 2011, pp. 88–92. ACM 2011Google Scholar
  29. 29.
    He, L., Wu, C., Wu, J., Xie, M., Huang, L., Ye, G.: Linked course data-based user personal knowledge recommendation engine. J. Comput. Inf. Syst. (2013)Google Scholar
  30. 30.
    Nielsen, J., Landauer, T.K.: A mathematical model of the finding of usability problems. In: CHI 1993 Proceedings of the INTERACT 1993 and CHI 1993 Conference on Human Factors in Computing Systems, pp. 206–213 (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jean-Francois Lapointe
    • 1
    Email author
  • Heather Molyneaux
    • 1
  • Irina Kondratova
    • 1
  • Aida Freixanet Viejo
    • 1
  1. 1.Human-Computer Interaction TeamNational Research Council of Canada, Information and Communications TechnologiesOttawaCanada

Personalised recommendations