Recommender Systems in Technology Enhanced Learning

  • Nikos ManouselisEmail author
  • Hendrik Drachsler
  • Riina Vuorikari
  • Hans Hummel
  • Rob Koper


Technology enhanced learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This chapter attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.


Recommender System Adaptive System Collaborative Filter Recommendation Algorithm User Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Research of N. Manouselis was funded with support by the European Commission and more specifically, the project ECP-2006-EDU-410012 ‘Organic.Edunet: A Multilingual Federation of Learning Repositories with Quality Content for the Awareness and Education of European Youth about Organic Agriculture and Agroecology’ of the eContentplus Programme. Research of H. Drachsler was funded with support by the European Commission and more specifically, the project IST 027087 ‘TENCompetence’ of the FP6 Programme. Riina Vuorikari thanks the HS-säätiö for the stipend.


  1. 1.
    Aehnelt M., Ebert M. Beham G., Lindstaedt S., Paschen A.: A Socio-technical Approach towards Supporting Intra-organizational Collaboration. In: Dillenbourg P. and Sprecht M., (eds.) Times of convergence: Technologies across learning contexts. Proceedings of the 3rd European Conference on Technology Enhanced Learning (EC-TEL 2008), Maastricht, The Netherlands, LNCS 5192, pp. 33-38, Berlin: Springer (2008).Google Scholar
  2. 2.
    Anderson M., Ball M., Boley H., Greene S., Howse N., Lemire D., McGrath S.: RACOFI: A Rule-Applying Collaborative Filtering System. Paper presented at the conference IEEE/WIC COLA 2003, 13 October, Halifax, Canada.Google Scholar
  3. 3.
    Aroyo, L., Mizoguchi, R., and Tzolov, C.: OntoAIMS- Ontological Approach to Courseware Authoring. Paper presented at the International Conference on Computers in Education (ICCE 2003), 2-5 December, Hong Kong, China 410 Nikos Manouselis, Hendrik Drachsler, Riina Vuorikari, Hans Hummel and Rob KoperGoogle Scholar
  4. 4.
    Avancini H., Straccia U.: User recommendation for collaborative and personalised digital archives, International Journal of Web Based Communities 1(2), 163–175 (2005).CrossRefGoogle Scholar
  5. 5.
    Baldoni, M., Baroglio, C., Brunkhorst, I., Marengo, E., Patti, V.: Reasoning-Based Curriculum Sequencing and Validation: Integration in a Service-Oriented Architecture. In Proceedings of the 2nd European Conference on Technology Enhanced Learning (EC-TEL 2007), LNCS 4753, pp. 426. Berlin: Springer (2007).Google Scholar
  6. 6.
    Baudisch P.: Dynamic Information Filtering. PhD Thesis, GMD Forschungszentrum Informationstechnik GmbH, Sankt Augustin (2001).Google Scholar
  7. 7.
    Breese, J. S., Heckerman, D., and Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Cooper G. F. and Moral S., (eds.) Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 43-52, Morgan-Kaufmann, Californian, San Francisco (1998).Google Scholar
  8. 8.
    Brockett, R. G., & Hiemstra, R.: Self-direction in adult learning: perspectives on theory, research, and practice. London: Routledge (1991).Google Scholar
  9. 9.
    Brusilovsky, P.: Methods and techniques of adaptive hypermedia, User Modeling and User- Adapted Interaction 6(2-3), 87–129 (1996).CrossRefGoogle Scholar
  10. 10.
    Brusilovsky, P.: Adaptive hypermedia. User Modeling and User-Adapted Interaction 11(1- 2), 87–110 (2001).zbMATHCrossRefGoogle Scholar
  11. 11.
    Brusilovsky, P. and Eklund, J.: A study of user-model based link annotation in educational hypermedia, Journal of Universal Computer Science 4(4), 429–448 (1998).Google Scholar
  12. 12.
    Brusilovsky P., Nejdl W.: Adaptive Hypermedia and Adaptive Web. Practical Handbook of Internet Computing, Chapman & Hall / CRC Press LLC (2005).Google Scholar
  13. 13.
    Brusilovsky, P., Karagiannidis, C., and Sampson, D. G.: The benefits of layered evaluation of adaptive applications and services. In: Weibelzahl, S., Chin, D. N., and Weber, G. (eds.) Empirical Evaluation of Adaptive Systems. Proceedings of 8th International Conference on User Modeling, (UM2001), pp 1-8, Berlin: Springer (2001).Google Scholar
  14. 14.
    Brusilovsky, P., Pesin, L., & Zyryanov, M.: Towards an adaptive hypermedia component for an intelligent learning environment. In: Bass, L.J., Gornostaev, J., & Unger, C. (eds.) Human-Computer Interaction (LNCS 753). pp. 348-358, Berlin: Springer (1993).Google Scholar
  15. 15.
    Brusilovsky P., Karagiannidis C., Sampson D., Layered evaluation of adaptive learning systems. International Journal of Continuing Engineering Education and Lifelong Learning 14(4/5), 402–421 (2004).CrossRefGoogle Scholar
  16. 16.
    Brusilovsky, P., & Henze, N.: Open Corpus Adaptive Educational Hypermedia. In P. Brusilovsky, A. Kobsa & W. Nejdl (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. (LNCS 4321), pp. 671-696 Berlin: Springer (2007).Google Scholar
  17. 17.
    Colley, H., Hodkinson, P., & Malcolm, J.: Non-formal learning: mapping the conceptual terrain. A consultation report. (2008). Accessed 11 January 2010.Google Scholar
  18. 18.
    Cristea, A.: Authoring of Adaptive Hypermedia. Educational Technology & Society 8(3), 6–8 (2005).Google Scholar
  19. 19.
    De Bra, P. M. E.: Teaching Hypertext and Hypermedia through theWeb. Journal of Universal Computer Science 2(12), 797–804 (1996).Google Scholar
  20. 20.
    De Bra, P., Aerts, A., Smits, D., & Stash, N.: AHA! Version 2.0, More Adaptation Flexibility for Authors. Paper presented at the World Conference on e-Learning in Corporate, Government, Healthcare & Higher Education. 15-19 October 2002, Montreal, Canada (2002).Google Scholar
  21. 21.
    De La Passardiere, B., & Dufresne, A.: Adaptive navigational tools for educational hypermedia. In: I. Tomek (ed.), Computer Assisted Learning. Proceedings of the 4th International Conference on Computers and Learning (ICCAL’92), LNCS 602, pp. 555-567, Berlin: Springer (1992).Google Scholar
  22. 22.
    Deshpande, M., & Karypis, G.: Selective Markov models for predicting Web page accesses. Transactions on Internet Technology. 4(2), 163–184 (2004).CrossRefGoogle Scholar
  23. 23.
    Dix, A. J., Finlay, J. E., Abowd, G. D., and Beale, R.: Human-Computer Interaction. Harlow, England: Prentice Hall (1998).Google Scholar
  24. 24.
    Drachsler H., Hummel H. G. K., Koper R.: Personal recommender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology 3(4), 404–423 (2008a).CrossRefGoogle Scholar
  25. 25.
    Drachsler, H., Hummel, H.G.K., Koper, R..: Using Simulations to Evaluate the Effects of Recommender Systems for Learners in Informal Learning Networks. In: Vuorikari, R., Kieslinger, B., Klamma, R., Duval, E. (eds.): SIRTEL workshop at the 3rd EC-TEL conference. CEUR Workshop Proceedings VOL-382, Maastricht, The Netherlands (2008b).Google Scholar
  26. 26.
    Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., Van Rosmalen, P., Hummel, H.G.K., Koper, R.: ReMashed-Recommendations for Mash-Up Personal Learning Environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.): Learning in the Synergy of Multiple Disciplines, Proceedings of the 4th European Conference on Technology Enhanced Learning (EC-TEL 2009), LNCS 5794, pp. 788-793, Berlin: Springer (2009a).Google Scholar
  27. 27.
    Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., Van Rosmalen, P., Hummel, H.G.K., Koper, R.: ReMashed-An Usability Study of a Recommender System for Mash- Ups for Learning. 1st Workshop on Mashups for Learning at the International Conference on Interactive Computer Aided Learning, Villach, Austria (2009b).Google Scholar
  28. 28.
    Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Berlanga, A., Nadolski, R., Waterink, W., Boers, N., & Koper, R..: Effects of the ISIS Recommender System for navigation support in self-organized learning networks. Journal of Educational Technology and Society 12, 122–135 (2009c).Google Scholar
  29. 29.
    Dron, J., Mitchell, R., Boyne, C., & Siviter, P.: CoFIND: steps towards a self-organising learning environment. Proceedings of the World Conference on the WWW and Internet (WebNet 2000), San Antonio, Texas, USA, October 30-November 4, pp. 146-151, USA AACE (2000a).Google Scholar
  30. 30.
    Dron, J., Mitchell, R., Siviter, P., & Boyne, C.: CoFIND-an experiment in n-dimensional collaborative filtering. Journal of Network and Computer Applications 23(2), 131–142 (2000b).CrossRefGoogle Scholar
  31. 31.
    Duval E., Vuorikari R., Manouselis N. (Eds.), Special Issue on Social Information Retrieval in Technology Enhanced Learning, Journal of Digital Information (JoDI), Vol. 10, (2009).Google Scholar
  32. 32.
    Fiaidhi J.: RecoSearch: A Model for Collaboratively Filtering Java Learning Objects. International Journal of Instructional Technology and Distance Learning 1(7), 35–50 (2004).Google Scholar
  33. 33.
    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
  34. 34.
    Gonschorek, M., & Herzog, C.: Using hypertext for an adaptive helpsystem in an intelligent tutoring system. In: J. Greer (Ed.), Artificial Intelligence in Education, Proceedings of the 7th World Conference on Artificial Intelligence in Education, (AI-ED’95), 16-19 August, Washington, DC, AACE, pp. 274 (1995).Google Scholar
  35. 35.
    Gordon, D.: Ants at Work: How an Insect Society is Organized. Free Press, New York (1999).Google Scholar
  36. 36.
    Graham, A.: Blended learning systems: definitions, current trends and future directions. In: Bonk, C. J. & Graham, C. R. (eds.) Handbook of blended learning: Global Perspectives, local designs. San Francisco, CA: Pfeiffer Publishing, pp. 3-21 (2005).Google Scholar
  37. 37.
    Herlocker, J., Konstan, J., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval 5(4), 287–310 (2002).CrossRefGoogle Scholar
  38. 38.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., & Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004).CrossRefGoogle Scholar
  39. 39.
    Hohl, H., Böcker, H.-D., Gunzenh¨auser, R.: Hypadapter: An adaptive hypertext system for exploratory learning and programming. User Modeling and User-Adapted Interaction 6(2- 3), 131–156 (1996).CrossRefGoogle Scholar
  40. 40.
    Höök, K.: Steps to take before intelligent user interfaces become real. InteractingWith Computers 12(4), 409–426 (2000).CrossRefGoogle Scholar
  41. 41.
    Huang, Y.-M., Huang, T.-C., Wang, K.-T., Hwang, W.-Y.: A Markov-based Recommendation Model for Exploring the Transfer of Learning on the Web. Educational Technology & Society 12(2), 144–162 (2009).Google Scholar
  42. 42.
    Hummel, H.G.K., Van den Berg, B., Berlanga, A.J., Drachsler, H., Janssen, J., Nadolski, R.J., Koper, E.J.R.: Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities. International Journal of Learning Technology 3(2), 152–168 (2007).CrossRefGoogle Scholar
  43. 43.
    Jameson, A.: Systems That Adapt to Their Users: An Integrative Perspective. Saar-br¨ucken: Saarland University (2001).Google Scholar
  44. 44.
    Janssen, J., Tattersall, C., Waterink, W., Van den Berg, B., Van Es, R., Bolman, C., et al.: Self-organising navigational support in lifelong learning: how predecessors can lead the way. Computers & Education 49, 781–793 (2005).CrossRefGoogle Scholar
  45. 45.
    Johnson, S.: Emergence. Scribner, New York (2001).Google Scholar
  46. 46.
    Karagiannidis, C., Sampson, D. G.: Layered evaluation of adaptive applications and services. In: Brusilovsky, P. and Stock, C. S. O. (Eds.), Proc. of International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH2000, Trento, Italy, pp. 343- 346. Berlin: Springer (2000).Google Scholar
  47. 47.
    Karampiperis, P., Sampson, D.: Adaptive Learning Resources Sequencing in Educational Hypermedia Systems. Educational Technology & Society 8(4), 128–147 (2005).Google Scholar
  48. 48.
    Kay, J., Kummerfeld, R. J.: An individualised course for the C programming language. In: Proceedings of Second International WWW Conference. Chicago, USA (1994).Google Scholar
  49. 49.
    Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. Educational Technology & Society 12(4), 30–42 (2009).Google Scholar
  50. 50.
    Kirkpatrick, D.L.: Evaluating Training Programs (2nd ed.). Berrett Koehler, San Francisco (1959).Google Scholar
  51. 51.
    Klamma, R., Spaniol, M., Cao, Y.: Community Aware Content Adaptation for Mobile Technology Enhanced Learning. In: Innovative Approaches for Learning and Knowledge Sharing, pp. 227-241 (2006).Google Scholar
  52. 52.
    Koper, R.: Increasing Learner Retention in a Simulated learning network using Indirect Social Interaction. Journal of Artificial Societies and Social Simulation, 8(2) (2005).Google Scholar
  53. 53.
    Koper, E.J.R., Tattersall, C.: New directions for lifelong learning using network technologies. British Journal of Educational Technology 35(6), 689–700 (2004).Google Scholar
  54. 54.
    Koper, R., Rusman, E., & Sloep, P.: Effective Learning Networks. Lifelong Learning in Europe 1, 18–27 (2005).Google Scholar
  55. 55.
    Koutrika, G., Ikeda, R., Bercovitz, B., Garcia-Molina, H.: Flexible Recommendations over Rich Data. In: Proc. of the 2nd ACM International Conference on Recommender Systems (Rec-Sys’08). Lausanne, Switzerland, (2008).Google Scholar
  56. 56.
    Koutrika, G., Bercovitz, B., Kaliszan, F., Liou, H., Garcia-Molina, H.: CourseRank: A Closed-Community Social System Through the Magnifying Glass. In: Proc. of the 3rd International AAAI Conference onWeblogs and Social Media (ICWSM’09). San Jose, California (2009).Google Scholar
  57. 57.
    Kravcik, M., Specht, M., Oppermann, R.: Evaluation of WINDS Authoring Environment. In: P. De Bra & W. Nejdl (Eds.), Adaptive Hypermedia and Adaptive Web-Based Systems, LNCS 3137, 166–175). Berlin: Springer (2004).Google Scholar
  58. 58.
    Krulwich, B.: Lifestyle Finder: Intelligent User Profiling Using Large-Scale Demographic Data. Artificial Intelligence Magazine 18(2), 37–45 (1997).Google Scholar
  59. 59.
    Kumar, V., Nesbit, J., Han, K.: Rating Learning Object Quality with Distributed Bayesian Belief Networks: The Why and the How. In: Proc. of the Fifth IEEE International Conference on Advanced Learning Technologies, ICALT’05 (2005).Google Scholar
  60. 60.
    Lemire, D.: Scale and Translation Invariant Collaborative Filtering Systems. Journal of Information Retrieval 8(1), 129–150 (2005).CrossRefMathSciNetGoogle Scholar
  61. 61.
    Lemire, D., Boley, H., McGrath, S., Ball, M. (2005). Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation. International Journal of Interactive Technology and Smart Education 2(3) (2005).Google Scholar
  62. 62.
    Liber, O.: Colloquia - a conversation manager. Campus-Wide Information Systems 17, 56– 61 (2000).CrossRefGoogle Scholar
  63. 63.
    Liber, O., Johnson, M. (2008). Personal Learning Environments. Interactive Learning Environments 16, 1–2 (2008).CrossRefGoogle Scholar
  64. 64.
    Longworth, N.: Lifelong learning in action - Transforming education in the 21st century. Kogan Page, London (2003).Google Scholar
  65. 65.
    Manouselis, N., Costopoulou, C.: Experimental Analysis of Design Choices in Multi- Attribute Utility Collaborative Filtering. International Journal of Pattern Recognition and Artificial Intelligence, Special Issue on Personalization Techniques for Recommender Systems and Intelligent User Interfaces 21(2), 311–331 (2007).Google Scholar
  66. 66.
    Manouselis, N., Vuorikari, R.: What if annotations were reusable: a preliminary discussion. In: Proc. of the 8th International Conference onWeb-based Learning (ICWL 2009). Aachen, Germany (2009).Google Scholar
  67. 67.
    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). Crete, Greece (2007).Google Scholar
  68. 68.
    McCalla, G.: The Ecological Approach to the Design of E-Learning Environments: Purposebased Capture and Use of Information About Learners. Journal of Interactive Media in Education, Special Issue on the Educational Semantic Web, 7, ISSN:1365-893X (2004).Google Scholar
  69. 69.
    McNee, S.: Meeting User Information Needs in Recommender Systems. Doctoral dissertation, University of Minnesota-Twin Cities, Minneapolis, MN, USA (2006).Google Scholar
  70. 70.
    Moore, J.D., Swartout,W.R.: Pointing: A way toward explanation dialogue. In: Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 457-464. AAAI (1990).Google Scholar
  71. 71.
    Moore, M.G., Anderson, W.G.: Handbook of distance education. Lawrence Erlbaum, Mahwah N.J. (2004).Google Scholar
  72. 72.
    Nadolski, R.J., Van den Berg, B., Berlanga, A., Drachsler, H., Hummel, H., Koper, R., Sloep, P.: Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies. Journal of Artificial Societies and Social Simulation (JASSS), 12(14) (2009).Google Scholar
  73. 73.
    Oppermann, R.: Adaptively supported adaptability. Int. J. Hum.-Comput. Stud. 40(3), 455– 472 (1994).CrossRefGoogle Scholar
  74. 74.
    Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences. In: Proceedings of the 3rd International Conference on Trust Management, pp. 224-239. Springer (2005).Google Scholar
  75. 75.
    Paramythis, A., Totter, A., Stephanidis, C.: A modular approach to the evaluation of adaptive user interfaces. In Weibelzahl, S., Chin, D., Weber, G. (eds.) Empirical Evaluation of Adaptive Systems. Proceedings of workshop at the Eighth International Conference on User Modeling, UM2001, pp. 9-24. Freiburg (2001).Google Scholar
  76. 76.
    Paris, C.: Tailoring object description to a user’s level of expertise. Computational Linguistics 14(3), 64–78 (1988).Google Scholar
  77. 77.
    P´erez, T., Guti´errez, J., Lopist´eguy, P.: An adaptive hypermedia system. In: Proceedings of AI-ED’95, 7th World Conference on Artificial Intelligence in Education, pp. 351-358. AACE (1995).Google Scholar
  78. 78.
    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
  79. 79.
    Rafaeli , S., Dan-Gur , Y., Barak , M.: Social Recommender Systems: Recommendations in Support of E-Learning. International Journal of Distance Education Technologies 3(2), 29–45 (2005).Google Scholar
  80. 80.
    Recker, M.M., Walker, A.: Supporting “Word-of-Mouth” Social Networks through Collaborative Information Filtering. Journal of Interactive Learning Research 14(1), 79–99 (2003).Google Scholar
  81. 81.
    Recker, M.M., Wiley, D.A.: A non-authoritative educational metadata ontology for filtering and recommending learning objects. Interactive learning environments 9(3), 255–271 (2001).CrossRefGoogle Scholar
  82. 82.
    Recker, M.M., Walker, A., Wiley, D.: An interface for collaborative filtering of educational resources. In: International Conference on Artificial Intelligence, pp. 26-29. Las Vegas, Nevada, USA (2000).Google Scholar
  83. 83.
    Recker, M.M., Walker, A., Lawless, K.: What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education. Instructional Science 31(4/5), 299–316 (2003).CrossRefGoogle Scholar
  84. 84.
    Rokach, L., Maimon, O., Averbuch, M., Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, page 217-228 Springer-Verlag (2004)Google Scholar
  85. 85.
    Rokach, L., Maimon, O., Arbel, R., Selective voting-getting more for less in sensor fusion, International Journal of Pattern Recognition and Artificial Intelligence 20(3), pp. 329–350 (2006)CrossRefGoogle Scholar
  86. 86.
    Santos, O.C.: A recommender system to provide adaptive and inclusive standard-based support along the eLearning life cycle. In: Proceedings of the 2008 ACM conference on Recommender systems, pp. 319-322 (2008).Google Scholar
  87. 87.
    Schneider-Hufschmidt, M., Kuhme, T., Malinowski, U.: Adaptive User Interfaces: Principles and Practice. Elsevier Science Inc (1993).Google Scholar
  88. 88.
    Shen, L., Shen, R.: Learning content recommendation service based-on simple sequencing specification. In: Liu W et al. (eds), pp. 363-370. Lecture notes in computer science (2004).Google Scholar
  89. 89.
    Tang T., McCalla G.: Smart Recommendation for an Evolving E-Learning System. In: Proc. of the Workshop on Technologies for Electronic Documents for Supporting Learning, International Conference on Artificial Intelligence in Education (AIED 2003) (2003).Google Scholar
  90. 90.
    Tang, T., McCalla, G.: Utilizing Artificial Learner on the Cold-Start Pedagogical-Value based Paper Recommendation. In: Proc. of AH 2004: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2004a).Google Scholar
  91. 91.
    Tang, T., McCalla, G.: Beyond Learners’ Interest: Personalized Paper Recommendation Based on Their Pedagogical Features for an e-Learning System. In: Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2004), pp. 301-310 (2004b).Google Scholar
  92. 92.
    Tang, T.Y., McCalla, G.: On the pedagogically guided paper recommendation for an evolving web-based learning system. In: Proceedings of the 17th International FLAIRS Conference, pp. 86-91 (2004c).Google Scholar
  93. 93.
    Tang, T., McCalla, G.: Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. International Journal on E-Learning 4(1), 105–129 (2005).Google Scholar
  94. 94.
    Totterdell, P., Boyle, E.: The evaluation of adaptive systems. Adaptive User Interfaces 161- 194 (1990).Google Scholar
  95. 95.
    Tsai, K.H., Chiu T.K., Lee M.C., Wang T.I.: A learning objects recommendation model based on the preference and ontological approaches. In: Proc. of 6th International Conference on Advanced Learning Technologies (ICALT’06). IEEE Computer Society Press (2006).Google Scholar
  96. 96.
    Tzikopoulos, A., Manouselis, N., Vuorikari, R.: An overview of Learning Object Repositories. In: Learning Objects for Instruction, Design and Evaluation, pp. 29-55. Idea Group Publishing, Hershey, PA (2007).Google Scholar
  97. 97.
    Van Bruggen, J., Sloep, P., van Rosmalen, P., Brouns, F., Vogten, H., Koper, R., Tattersall, C.: Latent semantic analysis as a tool for learner positioning in learning networks for lifelong learning. British Journal of Educational Technology 35(6), 729–738 (2004).CrossRefGoogle Scholar
  98. 98.
    Van Setten, M.: Supporting people in finding information: hybrid recommender systems and goal-based structuring. Telematica Instituut Fundamental Research Series NO. 016 (TI/FRS/016). Enschede, The Netherlands (2005).Google Scholar
  99. 99.
    Vuorikari, R., Berendt, B.: Study on contexts in tracking usage and attention metadata in multilingual Technology Enhanced Learning. In: Fischer, S., Maehle, E., Reischuk, R. (eds.) Im Focus das Leben, pp. 181, 1654-1663. Lecture Notes in Informatics (2009).Google Scholar
  100. 100.
    Vuorikari, R., Koper, R.: Ecology of social search for learning resources. Campus-Wide Information Systems 26(4), 272–286 (2009).CrossRefGoogle Scholar
  101. 101.
    Vuorikari, R., Ochoa, X.: Exploratory Analysis of the Main Characteristics of Tags and Tagging of Educational Resources in a Multi-lingual Context. Journal of Digital Information 10(2) (2009).Google Scholar
  102. 102.
    Vygotsky, L.: Mind in Society: The Development of Higher Psychological Processes. Harvard University Press (1978).Google Scholar
  103. 103.
    Waldrop, M.: Complexity: The Emerging Science at the Edge of Order and Chaos. Simons, Schuster, New York (1992).Google Scholar
  104. 104.
    Walker, A., Recker, M., Lawless, K., Wiley, D.: Collaborative information filtering: A review and an educational application. International Journal of Artificial Intelligence in Education 14(1), 3–28 (2004).Google Scholar
  105. 105.
    Wasserman, S., Faust, K.: Social network analysis: Methods and applications. Cambridge Univ Pr (1994).Google Scholar
  106. 106.
    Weibelzahl, S.: Evaluation of adaptive systems. In: User Modeling: Proceedings of the Eighth International Conference, UM2001, pp. 292-294 (2001).Google Scholar
  107. 107.
    Weibelzahl, S. Evaluation of adaptive systems. PhD dissertation. University of Trier, Germany (2003).Google Scholar
  108. 108.
    Weibelzahl, S., Paramythis, A., Totter, A.: A layered framework for the evaluation of interactive adaptive systems. In: Proc. 2nd Workshop on Empirical Evaluation of Adaptive Systems. Johnstown (2003).Google Scholar
  109. 109.
    Wild, F., Mödritscher, F., Sigurdarson, S.E.: Designing for change: mash-up personal learning environments. eLearning Papers. 9 (2008).Google Scholar
  110. 110.
    Wilson, S., Sharples, P., Griffiths, D.: Distributing education services to personal and institutional systems using Widgets. In: Mash-Up Personal Learning environments, Proceedings of the 1st MUPPLE workshop. CEUR-Proceedings (2008).Google Scholar
  111. 111.
    Worthen, B.R., Sanders, J.R., Fitzpatrick, J.L.: Program evaluation. Longman, New York (1997).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Nikos Manouselis
    • 1
    Email author
  • Hendrik Drachsler
    • 2
  • Riina Vuorikari
    • 3
  • Hans Hummel
    • 2
  • Rob Koper
    • 2
  1. 1.Greek Research and Technology Network (GRNET S.A.)AthensGreece
  2. 2.Centre for Learning Sciences and Technologies (CELSTEC)Open Universiteit NederlandHeerlenNetherlands
  3. 3.European Schoolnet (EUN)BrusselsBelgium

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