Advertisement

A Framework to Provide Personalization in Learning Management Systems through a Recommender System Approach

  • Hazra Imran
  • Quang Hoang
  • Ting-Wen Chang
  • Kinshuk
  • Sabine Graf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8397)

Abstract

Personalization in learning management systems (LMS) occurs when such systems tailor the learning experience of learners such that it fits to their profiles, which helps in increasing their performance within the course and the quality of learning. A learner’s profile can, for example, consist of his/her learning styles, goals, existing knowledge, ability and interests. Generally, traditional LMSs do not take into account the learners’ profile and present the course content in a static way to every learner. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners, not only based on their individual profile but also based on what worked well for learners with a similar profile. In this paper, we propose a framework to integrate a recommender system approach into LMS. The proposed framework is designed with the goal of presenting a flexible integration model which can provide personalization by automatically suggesting learning objects to learners based on their current situation as well as successful learning experiences of learners with similar profiles in a similar situation. Such advanced personalization can help learners in many ways such as reducing the learning time without negative impact on their marks, improving learning performance as well as increasing the level of satisfaction.

Keywords

Personalization E-Learning Learning Management Systems Recommender System 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Szabo, M.: CMI Theory and Practice: Historical Roots of Learning Management Systems. In: Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 929–936 (2002)Google Scholar
  2. 2.
    IEEE Learning Technology Standardization Committee. Draft standard for learning object metadata (IEEE 1484.12.1-2002). New York, NY (2002)Google Scholar
  3. 3.
    Brusilovsky, P., Miller, P.: Course Delivery Systems for the Virtual University. In: Tschang, F.T., Della Senta, T. (eds.) Access to Knowledge: New Information Technologies and the Emergence of the Virtual University, pp. 167–206. Elsevier (2001)Google Scholar
  4. 4.
    Shishehci, S., Banihashem, S.Y., Zin, N.A.M., Noah, S.A.M.: Review of Personalized Recommendation Techniques for Learners in learning management system Systems. In: Proc. of the Int. Conf. on Semantic Technology and Information Retrieval, pp. 277–281. IEEE Press (2011)Google Scholar
  5. 5.
    Huang, M.J., Huang, H.S., Chen, M.Y.: Constructing a Personalized Learning Management System based on Genetic Algorithm and Case-Based Reasoning Approach. Expert Systems with Applications 33(3), 551–564 (2007)CrossRefGoogle Scholar
  6. 6.
    Tseng, C.: Cluster-based Collaborative Filtering Recommendation Approach. Master’s Thesis, National Sun Yatsen University (2003)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. Internet Computing 7(1), 76–80 (2003)CrossRefGoogle Scholar
  10. 10.
    Capuano, N., Iannone, R., Gaeta, M., Miranda, S., Ritrovato, P., Salerno, S.: A Recommender System for Learning Goals. In: Lytras, M.D., Ruan, D., Tennyson, R.D., Ordonez De Pablos, P., García Peñalvo, F.J., Rusu, L. (eds.) WSKS 2011. CCIS, vol. 278, pp. 515–521. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Manouselis, N., Drachsler, H., Verbert, K., Duval, E.: Recommender Systems for Learning. Springer Briefs in Electrical and Computer Engineering. Springer (2012)Google Scholar
  12. 12.
    Zaïane, O.: Building a Recommender Agent for e-Learning Systems. In: Proceedings of the International Conference in Education, Auckland, New Zealand, pp. 55–59 (2002)Google Scholar
  13. 13.
    Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic Recommendations for E-Learning Personalization based on Web Usage Mining Techniques and Information Retrieval. In: Proc. of the Int. Conf. on Advanced Learning Technologies, pp. 241–245. IEEE Press (2008)Google Scholar
  14. 14.
    Tang, T., McCalla, G.: Smart Recommendation for an Evolving Learning Management System: Architecture and Experiment. International Journal on Learning Management System 4(1), 105–129 (2005)Google Scholar
  15. 15.
    Tai, D.W., Wu, H., Li, P.: Effective Learning Management System Recommendation System based on Self-Organizing Maps and Association Mining. The Electronic Library 26, 329–344 (2008)CrossRefGoogle Scholar
  16. 16.
    Kerkiri, T., Manitsaris, A., Mavridou, A.: Reputation Metadata for Recommending Personalized E-Learning Resources. In: Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization, pp. 110–115. IEEE Press (2007)Google Scholar
  17. 17.
    IEEE Learning Technology Standards Committee, http://www.ieeeltsc.org/
  18. 18.
    Yang, Q., Sun, J., Wang, J., Jin, Z.: Semantic Web-Based Personalized Recommendation System of Courses Knowledge Research. In: Proceedings of the International Conference on Intelligent Computing and Cognitive Informatics, pp. 214–217. IEEE Press (2009)Google Scholar
  19. 19.
    Dunn, R., Dunn, K., Freeley, M.E.: Practical Applications of the Research: Responding to Students’ Learning Styles – Step One. Illinois State Research and Development Journal 21(1), 1–21 (1984)Google Scholar
  20. 20.
    Felder, R.M., Soloman, B.A.: Index of Learning Styles Questionnaire. NorthCarolina State University (1996), http://www.engr.ncsu.edu/learningstyles/ilsweb.html
  21. 21.
    Felder, R.M., Silverman, L.K.: Learning and Teaching Styles in Engineering Education. Engineering Education 78(7), 674–681 (1988), Preceded by a preface in 2002, http://www4.ncsu.edu/unity/lockers/users/f/felder/public/Papers/LS-1988.pdf
  22. 22.
    Agrawal, R., Imieliński, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hazra Imran
    • 1
  • Quang Hoang
    • 1
  • Ting-Wen Chang
    • 1
  • Kinshuk
    • 1
  • Sabine Graf
    • 1
  1. 1.Athabasca UniversityEdmontonCanada

Personalised recommendations