QORECT – A Case-Based Framework for Quality-Based Recommending Open Courseware and Open Educational Resources

  • Monica Vladoiu
  • Zoran Constantinescu
  • Gabriela Moise
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8083)


More than a decade has passed since the start of the MIT OCW initiative, which, along with other similar projects, has been expected to change dramatically the educational paradigms worldwide. However, better findability is still expected for open educational resources and open courseware, so online guidance and services that support users to locate the appropriate such resources are most welcome. Recommender systems have a very valuable role in this direction. We propose here a hybrid architecture that combines enhanced case-based recommending (driven by a quality model tenet) with (collaborative) feedback from users to recommend open courseware and educational resources.


open courseware (OCW) open educational resources (OERs) quality model case-based reasoning recommendation system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kortemeyer, G.: Ten Years Later: Why Open Educational Resources Have Not Noticeably Affected Higher Education, and Why We Should Care, Educase Review online,
  2. 2.
    Vladoiu, M.: Quality Criteria for Open Courseware and Open Educational Resources. In: 11th ICWL 2012 Workshops. LNCS, vol. 7697. Springer, Heidelberg (in press, 2013)Google Scholar
  3. 3.
    Vladoiu, M., Constantinescu, Z.: Evaluation and Comparison of Three Open Courseware Based on Quality Criteria. In: Grossniklaus, M., Wimmer, M. (eds.) ICWE Workshops 2012. LNCS, vol. 7703, pp. 204–215. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Moise, G., Vladoiu, M., Constantinescu, Z.: MASECO - Multi-Agent System for Evaluation and Classification of OERs and OCW based on Quality Criteria (in press, 2013)Google Scholar
  5. 5.
    Nicoara, E.S.: The Impact of Massive Online Open Courses in Academic Environments. In: 9th Int. Conf. eLearning and Software for Education. Ed. Universitara, Bucharest (2013)Google Scholar
  6. 6.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H.G.K., Koper, R.: Recommender Systems in Technology Enhanced Learning. In: Kantor, P.B., Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender System Handbook, pp. 387–415. Springer, Berlin (2011)CrossRefGoogle Scholar
  7. 7.
    Lemire, D., Boley, H., McGrath, S., Ball, M.: Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation. International Journal of Interactive Technology and Smart Education 2(3), 179–188 (2005)CrossRefGoogle Scholar
  8. 8.
    Cechinel, C., Sicilia, M.-A., Sánchez Alonso, S., García Barriocanal, E.: Evaluating Collaborative Filtering Recommendations Inside Large Learning Object Repositories. Information Processing and Management 49(1), 34–50 (2013)CrossRefGoogle Scholar
  9. 9.
    Zapata, A., Menéndez, V.H., Prieto, M.E., Romero, C.: A Framework for Recommendation in Learning Object Repositories: An Example of Application in Civil Engineering. Advances in Engineering Software 56, 1–14 (2013)CrossRefGoogle Scholar
  10. 10.
    Resnick, P., Varian, H.R.: Recommender Systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  11. 11.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  12. 12.
    Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Burke, R.: Knowledge-based Recommender Systems. In: Kent, A. (ed.) Encyclopedia of Library and Information Systems, vol. 69(32). Marcel Dekker, New York (2000)Google Scholar
  15. 15.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating Contextual Information in Recommender Systems using a Multidimensional Approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005)CrossRefGoogle Scholar
  16. 16.
    Manouselis, N., Drachsler, H., Verbert, K.: TEL as a Recommendation Context, Recommender Systems for Learning, pp. 21–36. Springer, New York (2013)Google Scholar
  17. 17.
    Buder, J., Schwind, C.: Learning with Personalized Recommender Systems: A Psychological View. Computers in Human Behavior 28, 207–216 (2012)CrossRefGoogle Scholar
  18. 18.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7(1), 39–59 (1994)Google Scholar
  19. 19.
    Smyth, B.: Case-Based Recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Lee, J.S., Lee, J.C.: Context Awareness by Case-Based Reasoning in a Music Recommendation System. In: Ichikawa, H., Cho, W.-D., Satoh, I., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 45–58. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Văduva, I., Albeanu, G.: Introduction to fuzzy modelling. Ed. of Univ. of Bucharest (2004)Google Scholar
  22. 22.
    Nesbit, J.C., Li, J.Z., Leacock, T.L.: Web-Based Tools for Collaborative Evaluation of Learning Resources. J. of Systemics, Cybernetics and Informatics 3(5), 102–112 (2005)Google Scholar
  23. 23.
    Burgos Aguilar, J.V.: Rubrics to evaluate OERs (2011),
  24. 24.
  25. 25.
  26. 26.
    Vladoiu, M., Constantinescu, Z.: A Taxonomy of Opportunities for Searching, Browsing, and Retrieving OCW and OERs (submitted for publication, 2013)Google Scholar
  27. 27.
    Rafaeli, S., Barak, M., Dan-Gur, Y., Toch, E.: QSIA: a Web-based Environment for Learning, Assessing and Knowledge Sharing in Communities. Computers & Education 43(3), 273–289 (2004)CrossRefGoogle Scholar
  28. 28.
    Manouselis, N., Vuorikari, R., Van Assche, F.: Simulated Analysis of MAUT Collaborative Filtering for Learning Object Recommendation. In: Proc. of the Workshop on Social Information Retrieval in Technology Enhanced Learning (SIRTEL 2007), pp. 17–20 (2007)Google Scholar
  29. 29.
    Fiaidhi, J.: RecoSearch: a Model for Collaboratively Filtering Java Learning Objects. Int. J. Instruct. Technol. Distance Learning 1(7), 35–50 (2004)Google Scholar
  30. 30.
    Tang, T.Y., McCalla, G.I.: Smart Recommendation for an Evolving e-Learning System: Architecture and Experiment. Int. J. E-Learning 4, 105–129 (2005)Google Scholar
  31. 31.
    Ruiz-Iniesta, A., Jimenez-Diaz, G., Gómez-Albarrán, M.: Recommendation in Repositories of Learning Objects. In: The 9th IEEE International Conference on Advanced Learning Technologies (ICALT 2009), pp. 543–545 (2009)Google Scholar
  32. 32.
    Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative Filtering Adapted to Recommender Systems of E-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)CrossRefGoogle Scholar
  33. 33.
    Kumar, V., Nesbit, J., Winne, P., Hadwin, A., Jamieson-Noel, D., Han, K.: Quality Rating and Recommendation of Learning Objects. In: Pierre, S. (ed.) E-learning Networked Environments and Architectures, pp. 337–373. Springer, London (2007)CrossRefGoogle Scholar
  34. 34.
    Gomez-Albarran, M., Jimenez-Diaz, G.: Recommendation and Students’Authoring in Repositories of Learning Objects: A Case-Based Reasoning Approach. International Journal of Emerging Technologies in Learning (iJET) 4(1), 35–40 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Monica Vladoiu
    • 1
  • Zoran Constantinescu
    • 1
  • Gabriela Moise
    • 1
  1. 1.UPG University of PloiestiRomania

Personalised recommendations