Skip to main content
SpringerLink
Log in

Panorama of Recommender Systems to Support Learning

  • Chapter
Recommender Systems Handbook

Abstract

This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000–2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into seven clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abel, F., Bittencourt, I.I., de Barros Costa, E., Henze, N., Krause, D., Vassileva, J.: Recommendations in Online Discussion Forums for E-Learning Systems. TLT 3(2), 165–176 (2010)

    Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. Data Engin., 17(6), 734–749 (2005)

    Article  Google Scholar 

  3. Aher, S.B., Lobo, L.: Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data. Knowl.-Based Syst. 51: 1–14 (2013)

    Article  Google Scholar 

  4. Avancini, H., Straccia, U.: User recommendation for collaborative and personalised digital archives. International Journal of Web Based Communities, 1(2), 163–175 (2005)

    Article  Google Scholar 

  5. Beham, G., Kump, B., Ley, T., Lindstaedt, S.: Recommending knowledgeable people in a work-integrated learning system. Procedia Computer Science, 1(2), 2783–2792 (2010)

    Article  Google Scholar 

  6. Bielikova, M., Simko, M., Barla, M., Tvarozek, J., Labaj, M., Moro, R., Srba, I., & Sevcech, J.: ALEF: from Application to Platform for Adaptive Collaborative Learning. Special issue on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)

    Google Scholar 

  7. Bodea, C., Dascalu, M., Lipai, A.: Clustering of the Web Search Results in Educational Recommender Systems. In: Santos O, Boticario J (eds) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 154–181 (2012)

    Google Scholar 

  8. Boticario, J. G., Rodriguez-Ascaso, A., Santos, O. C., Raffenne, E., Montandon, L., Roldon, D., Buendia, F.: Accessible Lifelong Learning at Higher Education: Outcomes and Lessons Learned at two Different Pilot Sites in the EU4ALL Project. In Journal of Universal Computer Science 18 (1), 62–85 (2012).

    Google Scholar 

  9. Bozo, J., Alarcon, R., Iribarra, S. (2010) Recommending Learning Objects According to a Teachers Context Model. Sustaining TEL: From Innovation to Learning and Practice. Lecture Notes in Computer Science Volume 6383, 2010, pp 470–475

    Article  Google Scholar 

  10. Broisin, J., Brut, M., Butoianu, V., Sedes, F., Vidal, P.: A personalised recommendation framework based on CAM and document annotations. Procedia Computer Science, 1(2), 2839–2848 (2010)

    Article  Google Scholar 

  11. Brusilovsky, P., Cassel, L.N., Delcambre, L.M.L., Fox, E.A., Furuta, R., Garcia, D.D., Shipman III, F.M., Yudelson, M.: Social navigation for educational digital libraries, Procedia Computer Science, 1(2), 2889–2897 (2010)

    Article  Google Scholar 

  12. Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Model. User Adapt. Inter., 12, 331–370 (2002)

    Article  MATH  Google Scholar 

  13. Carchiolo, V., Longheu, A., Malgeri, M.: Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment, Information Sciences, Volume 180, Issue 10, pp. 1893–1907 (2010), ISSN 0020–0255, http://dx.doi.org/10.1016/j.ins.2009.12.023.

  14. Casali, A., Gerling, V., Deco, C., Bender, C.: A Recommender System for Learning Objects Personalized Retrieval. In: Santos O, Boticario J (eds) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 182–210. (2012) doi:10.4018/978-1-61350-489-5.ch008

    Google Scholar 

  15. Cazella, S.C., Reategui, E.B., Behar, P.A.: Recommendation of Learning Objects Applying Collaborative Filtering and Competencies. Key Competencies in the Knowledge Society pp. 35–43 (2010)

    Google Scholar 

  16. Cechinel, C., da Silva Camargo, S., Sánchez-Alonso, S., Sicilia, MA.: Towards automated evaluation of learning resources inside repositories. Special issue on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)

    Book  Google Scholar 

  17. Chen, C.M., Duh, L.-J.: Personalized web-based tutoring system based on fuzzy item response theory, Expert Systems with Applications, Volume 34, Issue 4, May 2008, pp. 2298–2315, ISSN 0957-4174, http://dx.doi.org/10.1016/j.eswa.2007.03.010 (2008)

  18. Chu, K., Chang, M., & Hsia, Y.: Designing a course recommendation system on web based on the students? course selection records. World conference on educational Educational Multimedia, Hypermedia and Telecommunications, EDMEDIA 2003 (pp. 4–21). Retrieved from http://www.editlib.org/p/18882/ (2003)

  19. dAquin, M., Dietze, S., Drachsler, H., Taibi, D.: Using linked data in learning analytics. eLearning Papers, No. 36, ISSN: 1887–1542, www.openeducationeuropa.eu/en/elearning_papers (2014)

  20. Diaz, A., Motz, R., Rohrer, E., Tansini, L.: An Ontology Network for Educational Recommender Systems. In: Santos, O., Boticario, J. (eds) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 67–93. doi:10.4018/978-1-61350-489-5.ch004 (2012)

    Google Scholar 

  21. Dietze, S., Drachsler, H., Giordano, D.: A Survey on Linked Data and the Social Web as facilitators for TEL RecSys. Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Eds: Manouselis, N., Verbert, K., Drachsler, H., Santos, O.C., Springer, Berlin (2013)

    Google Scholar 

  22. Dourado, A. O., and Martin, C. A.: New concept of dynamic flight simulator, Part I. Aerospace Science and Technology, 30(1), 79–82 (2013)

    Article  Google Scholar 

  23. 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. In: 1st Workshop on Mashups for Learning at the International Conference on Interactive Computer Aided Learning, Villach, Austria (2009)

    Google Scholar 

  24. 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. Educational Technology and Society, 12, pp. 122–135 (2009)

    Google Scholar 

  25. Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Beham, G., Lindstaedt, S., Stern, H., Friedrich, M.: Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning. In: Procedia Computer Science, 1(2), pp. 2849–2858. doi:10.1016/j.procs.2010.08.010 (2010)

    Google Scholar 

  26. Drachsler, H., K. Verbert, N. Manouselis, R. Vuorikari, M. Wolpers, S. Lindstaedt. Preface [Special Issue on dataTEL - Data Supported Research in Technology-Enhanced Learning]. In: International Journal Technology Enhanced Learning 4 (1/2) (2012)

    Google Scholar 

  27. Drachsler, H., Li, Y., Santos, O.C.: Recommender Systems for Learning. In: Sampson, D. G., Spector, J. M., Chen, N.S., Huang, R., Kinshuk, editor, Proceedings of the IEEE 14th International Conference on Advanced Learning Technologies, pp. 513–538. IEEE (2014).

    Google Scholar 

  28. Dron, J., Mitchell, R., Siviter, P., Boyne, C.: CoFIND-an experiment in n-dimensional collaborative filtering. Journal of Network and Computer Applications, 23(2), pp. 131–142 (2000)

    Article  Google Scholar 

  29. El-Bishouty MM, Ogata H, Yano Y (2007) Perkam: Personalized knowledge awareness map for computer supported ubiquitous learning. Educational Technology and Society, 10(3):122–134

    Google Scholar 

  30. El Helou, S., Salzmann, C., Gillet, D.: The 3A personalised, contextual and relation-based recommender system. Journal of Universal Computer Science, 16(16), 2179–2195 (2010)

    Google Scholar 

  31. Fazeli, S., Loni, B., Drachsler, D., & Sloep, P. B. (2014). Which Recommender System Can Best Fit Social Learning Platforms?. In Proceedings of the Ninth European Conference on Technology Enhanced Learning, Open Learning and Teaching in Educational Communities (EC-TEL2014), Graz, Austria.

    Google Scholar 

  32. Fazeli, S., Drachsler, H., Brouns, F., Sloep, P. (2014) Towards a Social Trust-Aware Recommender for Teachers. Recommender Systems for Technology Enhanced Learning, Springer, 177–194

    Google Scholar 

  33. Fernandez, A., Anjorin, M., Dackiewicz, I., and Rensing, C.: Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem. Special issue on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)

    Book  Google Scholar 

  34. 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 

  35. Fraij, F., Al-Dmour, A., Al-Hashemi, R., Musa, A.: An evolving recommender-based framework for virtual learning communities. IJWBC 8(3): 322–332 (2012)

    Article  Google Scholar 

  36. Farzan, R., Brusilovsky, P.: Encouraging user participation in a course recommender system: An impact on user behavior. Computers in Human Behavior, 27(1), pp. 276–284 (2011)

    Article  Google Scholar 

  37. Gallego, D.; Barra, E.; Gordillo, A; Huecas, G.: Enhanced recommendations for e-Learning authoring tools based on a proactive context-aware recommender. In: IEEE Frontiers in Education Conference, 1393,1395 (2013)

    Google Scholar 

  38. Garcia, E., Romero, C., Ventura, S., de Castro, C.: An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, 19(1–2), 99–132 (2009)

    Article  Google Scholar 

  39. Ghauth, K. I., & Abdullah, N. A.: The Effect of Incorporating Good Learners’ Ratings in e-Learning Content-based Recommender System. Educational Technology & Society, 14 (2), 248–257 (2011)

    Google Scholar 

  40. 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 

  41. Greller, W., Drachsler, H.: Translating Learning into Numbers: A Generic Framework for Learning Analytics. In: Educational Technology & Society, 15(3), pp. 42–57 (2012)

    Google Scholar 

  42. Han, P., Xie, B., Yang, F., Shen, R.: A scalable P2P recommender system based on distributed collaborative filtering. Expert Systems with Applications, 27, pp. 203–210 (2004)

    Article  Google Scholar 

  43. Hanani, U., Shapira, B., Shoval, P.: Information Filtering: Overview of Issues, Research and Systems. User Modeling and User-Adapted Interaction, 11, 203–259 (2001)

    Article  MATH  Google Scholar 

  44. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 22, 1, pp. 5–53 (2004)

    Article  Google Scholar 

  45. Holanda, O., Ferreira, R., Costa, E., Bittencourt, I.I., Melo, J., Peixoto, M., Tiengo, W.: Educational resources recommendation system based on agents and semantic web for helping students in a virtual learning environment. IJWBC 8(3), pp. 333–353 (2012)

    Article  Google Scholar 

  46. Hsieh, T.-C., Lee, M.-C., Su, C.-Y.: Designing and implementing a personalized remedial learning system for enhancing the programming learning. Educational Technology & Society 16(4): 32–46 (2013)

    Google Scholar 

  47. Hsu, C.-K., Hwang, G.-J., Chang, C.-K.: A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students, Computers & Education, Volume 63, April 2013, pp. 327–336, ISSN 0360-1315, http://dx.doi.org/10.1016/j.compedu.2012.12.004 (2013)

  48. 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 and Society, 12(2),144–162 (2009)

    Google Scholar 

  49. 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 and Education, 49(3), pp. 781–793 (2007)

    Article  Google Scholar 

  50. Kaklauskas, A., Zavadskas, E.K., Seniut, M., Stankevic, V., Raistenskis, J., Simkevioius, C., Stankevic, T., Matuliauskaite, A., Bartkiene, L., Zemeckyte, L., Paliskiene, R., Cerkauskiene, R., Gribniak, V. Recommender System to Analyze Students Academic Performance. Expert Systems with Applications, 40(15), 6150–6165 (2013)

    Article  Google Scholar 

  51. Kalz, M., and Specht, M.: Assessing the crossdisciplinarity of technology-enhanced learning with science overlay maps and diversity measures. In: British Journal of Educational Technology, 18 p. (2013)

    Google Scholar 

  52. Karampiperis, P., Koukourikos, A., Stoitsis, G.: Collaborative Filtering Recommendation of Educational Content in Social Environments utilizing Sentiment Analysis Techniques. Special issue on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)

    Book  Google Scholar 

  53. Kerkiri, T., Manitsaris, A., Mavridis, I.: How e-learning systems may benefit from ontologies and recommendation methods to efficiently personalise resources. IJKL 5(3/4): 347–370 (2009)

    Article  Google Scholar 

  54. Khribi, M.K., Jemni, M., Nasraoui, O.: Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. Educational Technology and Society, 12(4), pp. 30–42 (2009)

    Google Scholar 

  55. 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 on Weblogs and Social Media (ICWSM’09). San Jose, California (2009)

    Google Scholar 

  56. Leino, J.: Case study: recommending course reading materials in a small virtual learning community. IJWBC 8(3): 285–301 (2012)

    Article  Google Scholar 

  57. 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), (2005)

    Google Scholar 

  58. Li, M., Ogata, H., Hou, B, Uosaki, N., Mouri, K. Context-aware and Personalization Method in Ubiquitous Learning Log System. Educational Technology & Society, 16 (3), 362–373 (2013)

    Google Scholar 

  59. Limongelli, C., Lombardi, M., Marani, A., Sciarrone, F. (2013) A Teaching-Style Based Social Network for Didactic Building and Sharing. AIED 2013, LNAI 7926, pp. 774–777, 2013.

    Google Scholar 

  60. Luo, F., Dong, J., Cao, A.: Song. A context-aware personalized resource recommendation for pervasive learning. Cluster Computing, June 2010, Volume 13, Issue 2, pp 213–239 (2010)

    Google Scholar 

  61. Mangina, E.E., Kilbride, J.: Evaluation of keyphrase extraction algorithm and tiling process for a document/resource recommender within e-learning environments. Computers & Education, 50(3), pp. 807–820 (2008)

    Article  Google Scholar 

  62. 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), pp. 311–333 (2007)

    Article  Google Scholar 

  63. 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 

  64. Manouselis, N., Vuorikari, R., Van Assche, F.: Collaborative Recommendation of e-Learning Resources: An Experimental Investigation. In: Journal of Computer Assisted Learning, Special Issue on Adaptive technologies and methods in e/m-Learning and Internet-based education, Blackwell Publishing Ltd., 26(4), pp. 227–242, (2010)

    Google Scholar 

  65. Manouselis, N., Drachsler, H., Verbert, K., Santos, O.C. (Eds.) Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010). Procedia Computer Science, Volume 1, Issue 2, Pages 2773–2998 (2010)

    Google Scholar 

  66. Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., and Koper, R.: Recommender systems in technology enhanced learning. In: Rokach, L., Shapira, B., Kantor, P., Ricci, F., editor, Recommender Systems Handbook: A Complete Guide for Research Scientists & Practitioners, pp. 387–409. Springer (2011)

    Google Scholar 

  67. Manouselis, N., Drachsler, H., Verbert, K., and Duval, E.: Recommender Systems for Learning. Berlin, Springer, 2012, 90 p.

    Google Scholar 

  68. Manouselis, N., Drachsler, H., Verbert, K., and Santos, O.: Proceedings of the 2nd Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2012). CEUR workshop proceedings, Vol-896, 100 p. (2012)

    Google Scholar 

  69. Manouselis, N., Drachsler, H., Verbert, K., Santos, O.C.: Recommender Systems for Technology Enhanced Learning: Research Trends & Applications. Springer (2014)

    Google Scholar 

  70. Marino, O., Paquette, G.: A competency-driven advisor system for multi-actor learning environments. Procedia Computer Science, 1(2):2871–2876, doi:10.1016/j.procs.2010.08.013 (2010)

    Article  Google Scholar 

  71. Martin, E., Carro, R.M.: Supporting the Development of Mobile Adaptive Learning Environments: A Case Study. TLT 2(1): 23–36 (2009)

    Google Scholar 

  72. Masters, K.: A brief guide to understanding MOOCs”. The Internet Journal of Medical Education 1 (Num. 2) (2011)

    Google Scholar 

  73. Michlik, P., Bielikova, M.: Exercises recommending for limited time learning. Procedia Computer Science, (1)2:2821–2828. doi:10.1016/j.procs.2010.08.007 (2010)

    Google Scholar 

  74. Moedritscher, F.: Towards a recommender strategy for personal learning environments. Procedia Computer Science, (1)2:2775–2782. doi:10.1016/j.procs.2010.08.002 (2010)

    Google Scholar 

  75. Montaner, M., Lopez, B., de la Rosa, J.L.: A Taxonomy of Recommender Agents on the Internet. Artif. Intell. Rev., 19, pp. 285–330 (2003)

    Article  Google Scholar 

  76. 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 

  77. Nowakowski, S., Ognjanovic, I., Grandbastien, M., Jovanovic, J., Sendelj, R.: Two Recommending Strategies to enhance Online Presence in Personal Learning Environments. Special issue on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)

    Book  Google Scholar 

  78. Nussbaumer, A., Berthold, M., Dahrendorf, D., Schmitz, H..C., Kravcik, M., Albert, D.: A Mashup Recommender for Creating Personal Learning Environments. Advances in Web-Based Learning - ICWL 2012. Lecture Notes in Computer Science Volume 7558, pp. 79–88. doi: 10.1007/978-3-642-33642-3_9 (2012)

    Article  Google Scholar 

  79. Okoye, I., Maull, K., Foster, J., Sumner, T.: Educational Recommendation in an Informal Intentional Learning System. In: Santos O, Boticario J (eds), Educational Recommender Systems and Technologies: Practices and Challenges, pp. 1–23. doi:10.4018/978-1-61350-489-5.ch001 (2012)

    Google Scholar 

  80. O’Mahony, M.P., Smyth, B.: A recommender system for on-line course enrolment: an initial study. RecSys 2007, pp. 133–136 (2007)

    Article  Google Scholar 

  81. Rafaeli, S., Dan-Gur, Y., Barak, M.: Social Recommender Systems: Recommendations in Support of E-Learning. International Journal of Distance Education Technologies, 3(2), pp. 29–45 (2005)

    Article  Google Scholar 

  82. Recker, M.M., Walker, A.: Supporting “Word-of-Mouth” Social Networks through Collaborative Information Filtering. Journal of Interactive Learning Research, 14(1), pp. 79–99 (2003)

    Google Scholar 

  83. Romero, C., Ventura, S., Zafra, A., De Bra, P.: Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Computers & Education 53(3), pp. 828–840 (2009)

    Article  Google Scholar 

  84. Salehi, M.: Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation, Data & Knowledge Engineering, Volume 87, September 2013, pp. 130–145, ISSN 0169-023X, http://dx.doi.org/10.1016/j.datak.2013.07.001 (2013)

  85. 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. ACM (2008)

    Google Scholar 

  86. Santos, O. C., & Boticario, J. G.: Educational Recommender Systems and Technologies: Practices and Challenges (pp. 1–362). Hershey, PA: IGI Global. doi:10.4018/978-1-61350-489-5 (2012)

    Book  Google Scholar 

  87. Santos, O. C., & Boticario, J. G.: Special Issue on Recommender Systems to Support the Dynamics of Virtual Learning Communities. International Journal of Web Based Communities, Vol. 8 No. 3 (2012)

    Google Scholar 

  88. Santos, O.C., Boticario, J.G.: User Centred Design and Educational Data Mining support during the Recommendations Elicitation Process in Social Online Learning Environments. 32(2), 293–311, (2015). DOI: 10.1111/exsy.12041

  89. Santos, O.C., Boticario, J.G., Pérez-Marin, D.: Extending web-based educational systems with personalised support through User Centred Designed recommendations along the e-learning life cycle, Science of Computer Programming, Volume 88, Pages 92–109, ISSN 0167-6423. (2014)

    Google Scholar 

  90. Santos, O.C., Boticario, J.G., Manjarrés-Riesco, A.: An Approach for an Affective Educational Recommendation Model. Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, pp 123–143, Springer Berlin (2014)

    Google Scholar 

  91. Santos, O. C., Boticario, J.G.: Exploring Arduino for Building Educational Context-Aware Recommender Systems that Deliver Affective Recommendations in Social Ubiquitous Networking Environments. In Proceedings of Web-Age Information Management. Lecture Notes in Computer Science, Volume 8597, 2014, pp 272–286.

    Article  Google Scholar 

  92. Santos, O. C., Saneiro, M., Boticario, J., Rodriguez-Sanchez, C. Towards Interactive Context-Aware Affective Educational Recommendations in Computer Assisted Language Learning. New Review of Hypermedia and Multimedia, pp. 1–31. http://dx.doi.org/10.1080/13614568.2015.1058428 (2015)

  93. Santos, O.C., Saneiro, M., Salmeron-Majadas, S., Boticario, J.G.: A methodological approach to eliciting affective educational recommendations. In Proceedings of the 14th IEEE International Conference on Advanced Learning Technologies (ICALT14), 529–533 (2014) doi: 10.1109/ICALT.2014.234

    Google Scholar 

  94. Schafer, J.B., Konstan, J.A., Riedl, J.: E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 5, pp. 115–153 (2001)

    Article  MATH  Google Scholar 

  95. Schoefegger, K., Seitlinger, P., Ley, T.: Towards a user model for personalised recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system. Procedia Computer Science, 1(2):2829–2838, doi:10.1016/j.procs.2010.08.008 (2010)

    Article  Google Scholar 

  96. Sergis, S., Zervas, P., Sampson, D.G. (2014) Towards Learning Object Recommendations based on Teachers ICT Competence Profiles. 2014 IEEE 14th International Conference on Advanced Learning Technologies, 534–538

    Google Scholar 

  97. Shelton, B.E., Duffin, J., Wang, Y., Ball, J.: Linking open course wares and open education resources: creating an effective search and recommendation system. Procedia Computer Science, 1(2), pp. 2865–2870 doi:10.1016/j.procs.2010.08.012 (2010)

    Article  Google Scholar 

  98. Shen, L., Shen, R.: Learning content recommendation service based-on simple sequencing specification. In: Liu W et al. (eds) Lecture notes in computer science, pp. 363–370 (2004)

    Google Scholar 

  99. Sicilia, M.A., Garcia-Barriocanal, E., Sanchez-Alonso, S., Cechinel, C.: Exploring user-based recommender results in large learning object repositories: the case of MERLOT. Procedia Computer Science, 1(2), pp. 2859–2864. doi:10.1016/j.procs.2010.08.011 (2010)

    Article  Google Scholar 

  100. Sielis, G.A., Mettouris, C., Tzanavari, A., Papadopoulos, G.A.: Context-Aware Recommendations using Topic Maps Technology for the Enhancement of the Creativity Process. In: Santos O, Boticario J (eds) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 43–66. doi:10.4018/978-1-61350-489-5.ch003 (2012)

    Google Scholar 

  101. Tai, D.W.S., Wu, H.J., Li, P.H.: Effective e-learning recommendation system based on self-organizing maps and association mining. The Electronic Library, 26(3), 329–344 (2008)

    Article  Google Scholar 

  102. Tang, T.Y., McCalla, G.: Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. International Journal on E-Learning, 4(1), pp. 105–129 (2005)

    Google Scholar 

  103. Tang, TY., Winoto, P.,and McCalla, G.: Further Thoughts on Context-Aware Paper Recommendations for Education. Special issue on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)

    Book  Google Scholar 

  104. Tang, T.Y., Daniel, B.K., Romero, C.: Special Issue on Recommender systems for and in social and online learning environments. Expert Systems (2014)

    Google Scholar 

  105. Thai-Nghe, N., Drumond, L., Horvith, T., Krohn-Grimberghe, A., Nanopoulos, A., Schmidt-Thieme, L.: Factorization Techniques for Predicting Student Performance. In Santos O, Boticario J (eds) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 129–153. doi:10.4018/978-1-61350-489-5.ch006 (2012)

    Google Scholar 

  106. 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 

  107. Underwood, J.S.: Metis: A Content Map-Based Recommender System for Digital Learning Activities. In: Santos O, Boticario J (eds), Educational Recommender Systems and Technologies: Practices and Challenges, pp. 24–42. doi:10.4018/978-1-61350-489-5.ch002 (2012)

    Google Scholar 

  108. Vialardi Sacun, C., Bravo Agapito, J., Shafti, L., Ortigosa, A.: Recommendation in Higher Education Using Data Mining Techniques. EDM 2009: 191–199 (2009)

    Google Scholar 

  109. Verbert, K., Duval, E., Lindstaedt, S. and Gillet, D. (eds): Special issue on Context-aware Recommender Systems, Journal of Universal Computer Science, 16(16), pp. 2175–2290 (2010)

    Google Scholar 

  110. Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-Driven Research to Support Learning and Knowledge Analytics. Educational Technology & Society, 15 (3), 133–148.”

    Google Scholar 

  111. Verbert, K., Manouselis, N., Xavier, O., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-aware Recommender Systems for Learning: a Survey and Future Challenges. IEEE Transactions on Learning Technologies. 5(4), pp. 318–335 (2012)

    Article  Google Scholar 

  112. Vesin, B., Milicevic, A.K., Ivanovic, M., Budimac, Z.: Applying Recommender Systems and Adaptive Hypermedia for e-Learning Personalizatio. Computing and Informatics 32(3), pp. 629–659 (2013)

    Google Scholar 

  113. Vuorikari, R., Manouselis, N., and Duval, E. Special issue on social information retrieval for technology enhanced learning. Journal Of Digital Information, 10(2) (2009)

    Google Scholar 

  114. Wan, X., Okamoto, T.: Utilizing learning process to improve recommender system for group learning support. Neural Computing and Applications 20(5): 611–621 (2011)

    Article  Google Scholar 

  115. Wang, Y., Sumiya, K.: Semantic ranking of lecture slides based on conceptual relationship and presentational structure. Procedia Computer Science, 1(2), pp. 2801–2810. doi:10.1016/j.procs.2010.08.005 (2010)

    Article  Google Scholar 

  116. Wang, F.-H.: On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies. Expert Syst. Appl. 39(7), pp. 6446–6453 (2012)

    Article  Google Scholar 

  117. Wang, S.L., Wu, C.Y. Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications, 38(9), 10831–10838 (2011)

    Article  Google Scholar 

  118. Wei, C.-P., Shaw, M.J., Easley, R.F.: A Survey of Recommendation Systems in Electronic Commerce. In: Rust RT, Kannan PK (eds) E-Serv.: New Dir. in Theor. and Pract., M. E. Sharpe Publisher (2002)

    Google Scholar 

  119. Weidenbach M., Drachsler H., Wild F., Kreutter S., Razek V., Grunst G., Ender J., Berlage T., and Janousek J.: EchoComTEE a simulator for transoesophageal echocardiography. Anaesthesia, 62, 4, pp. 347–353 (2007)

    Article  Google Scholar 

  120. Weppner, J., Lukowicz, P., Hirth, M., Kuhn, J. Physics education with Google Glass gPhysics experiment app. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp ‘14 Adjunct), 279–282 (2014)

    Google Scholar 

  121. Yu, Z., Zhou, X., Shu, L.: Towards a semantic infrastructure for context-aware e-learning. Multimedia Tools Appl. 47(1): 71–86 (2010)

    Article  Google Scholar 

  122. Zaiane, O.R.: Building a recommender agent for e-learning systems. Computers in Education, 2002. vol.1, 3–6, doi: 10.1109/CIE.2002.1185862 (2002)

    Article  Google Scholar 

  123. Zaldivar, V.A., Burgos, D., Pardo, A.: Meta-Rule Based Recommender Systems for Educational Applications. In: Santos O, Boticario J (eds) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 211–231. doi:10.4018/978-1-61350-489-5.ch009 (2012)

    Google Scholar 

  124. Zapata, A., Menendez, 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)

    Article  Google Scholar 

  125. Zhou, M., Xu, Y.: Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners. In: Santos, O., Boticario, J. (eds) Educational Recommender Systems and Technologies: Practices and Challenges, pp. 282–301. doi:10.4018/978-1-61350-489-5.ch012 (2012)

    Google Scholar 

Download references

Acknowledgements

Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC-TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229).

Author information

Authors and Affiliations

  1. Welten Institute Research Centre for Learning, Teaching and Technology, Open University of the Netherlands, Heerlen, The Netherlands

    Hendrik Drachsler

  2. Department of Computer Science, KU Leuven, Leuven, Belgium

    Katrien Verbert

  3. Department of Computer Science, Vrije Universiteit Brussel, Brussel, Belgium

    Katrien Verbert

  4. aDeNu Research Group, UNED, Madrid, Spain

    Olga C. Santos

  5. Agro-Know, Vrilissia, Greece

    Nikos Manouselis

Authors
  1. Hendrik Drachsler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katrien Verbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olga C. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nikos Manouselis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hendrik Drachsler .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this chapter

Cite this chapter

Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N. (2015). Panorama of Recommender Systems to Support Learning. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7637-6_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4899-7636-9

  • Online ISBN: 978-1-4899-7637-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation