Panorama of recommender systems to support learning

Recommender Systems Handbook, Second Edition

Volumn , Issue , 2015, Pages 421-451

  Drachsler, H ^a   Verbert, K ^b,c   Santos, O C ^d   Manouselis, N ^e  

^a OPEN UNIVERSITY OF THE NETHERLANDS   (Netherlands)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^d UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

^e University of Alcala   (Greece)

Author keywords

Classification framework; Educational data mining; Educational datasets; Future challenges; Learning analytics; PersonalisationTrend analysis; Recommender systems; State of the art review; Technology enhanced learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); RECOMMENDER SYSTEMS;

CLASSIFICATION FRAMEWORK; EDUCATIONAL DATA MINING; EDUCATIONAL DATASETS; FUTURE CHALLENGES; LEARNING ANALYTICS; PERSONALISATIONTREND ANALYSIS; STATE-OF-THE ART REVIEWS; TECHNOLOGY ENHANCED LEARNING;

EDUCATION;

EID: 84956809282     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4899-7637-12     Document Type: Chapter

References (125)

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)
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)
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)
4. Avancini, H., Straccia, U.: User recommendation for collaborative and personalised digital archives. International Journal of Web Based Communities, 1(2), 163–175 (2005)
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)
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.on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)
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)
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).
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, 470–475
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)
11. Brusilovsky, P., Cassel, L.N., Delcambre, L.M.L., Fox, E.A., Furuta, R., Garcia, D. D. Shipman, F. M., Yudelson, M.: Social navigation for educational digital libraries, Procedia Computer Science, 1(2), 2889–2897 (2010)
12. Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Model. User Adapt. Inter., 12, 331–370 (2002)
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
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)
16. Cechinel, C., da Silva Camargo, S., Sánchez-Alonso, S., Sicilia, MA.: Towards automated evaluation of learning resources inside repositories.on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)
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)
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)
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)
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)
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)
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)
26. Drachsler, H., K. Verbert, N. Manouselis, R. Vuorikari, M. Wolpers, S. Lindstaedt. Preface [on dataTEL - Data Supported Research in Technology-Enhanced Learning]. In: International Journal Technology Enhanced Learning 4 (1/2) (2012)
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).
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)
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
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)
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.
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
33. Fernandez, A., Anjorin, M., Dackiewicz, I., and Rensing, C.: Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem.on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
52. Karampiperis, P., Koukourikos, A., Stoitsis, G.: Collaborative Filtering Recommendation of Educational Content in Social Environments utilizing Sentiment Analysis Techniques.on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)
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)
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)
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)
56. Leino, J.: Case study: recommending course reading materials in a small virtual learning community. IJWBC 8(3): 285–301 (2012)
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)
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)
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.
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)
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)
62. Manouselis, N., Costopoulou, C.: Experimental Analysis of Design Choices in Multi- Attribute Utility Collaborative Filtering. International Journal of Pattern Recognition and Artificial Intelligence, on Personalization Techniques for Recommender Systems and Intelligent User Interfaces, 21(2), pp. 311–333 (2007)
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)
64. Manouselis, N., Vuorikari, R., Van Assche, F.: Collaborative Recommendation of e-Learning Resources: An Experimental Investigation. In: Journal of Computer Assisted Learning, on Adaptive technologies and methods in e/m-Learning and Internet-based education, Blackwell Publishing Ltd., 26(4), pp. 227–242, (2010)
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)
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
67. Manouselis, N., Drachsler, H., Verbert, K., and Duval, E.: Recommender Systems for Learning. Berlin, Springer, 2012, 90 p.
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)
69. Manouselis, N., Drachsler, H., Verbert, K., Santos, O.C.: Recommender Systems for Technology Enhanced Learning: Research Trends & Applications. Springer (2014)
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)
71. Martin, E., Carro, R.M.: Supporting the Development of Mobile Adaptive Learning Environments: A Case Study. TLT 2(1): 23–36 (2009)
72. Masters, K.: A brief guide to understanding MOOCs”. The Internet Journal of Medical Education 1 (Num. 2) (2011)
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)
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)
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)
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)
77. Nowakowski, S., Ognjanovic, I., Grandbastien, M., Jovanovic, J., Sendelj, R.: Two Recommending Strategies to enhance Online Presence in Personal Learning Environments.on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)
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)
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)
80. O’Mahony, M.P., Smyth, B.: A recommender system for on-line course enrolment: an initial study. RecSys 2007, pp. 133–136 (2007)
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)
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)
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)
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-basedsupport along the eLearning life cycle. In: Proceedings of the 2008 ACM conference on Recommender systems, pp. 319–322. ACM (2008)
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)
87. Santos, O. C., & Boticario, J. G.:on Recommender Systems to Support the Dynamics of Virtual Learning Communities. International Journal of Web Based Communities, Vol. 8 No. 3 (2012)
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 elearning life cycle, Science of Computer Programming, Volume 88, Pages 92–109,. ISSN 0167-6423 (2014)
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)
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 ofWeb-Age Information Management. Lecture Notes in Computer Science, Volume 8597, 2014, pp 272–286.
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 approachto eliciting affective educational recommendations. In Proceedings of the 14th IEEE InternationalConference on Advanced Learning Technologies (ICALT14), 529–533 (2014) doi: 10.1109/ICALT.2014.234
94. Schafer, J.B., Konstan, J.A., Riedl, J.: E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 5, pp. 115–153 (2001)
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)
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
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)
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)
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)
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)
101. Tai, D.W.S., Wu, H.J., Li, P.H.: Effective e-learning recommendation system based on selforganizing maps and association mining. The Electronic Library, 26(3), 329–344 (2008)
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)
103. Tang, TY., Winoto, P. and McCalla, G.: Further Thoughts on Context-Aware Paper Recommendationsfor Education.on Recommender Systems for Technology Enhanced Learning: Research Trends & Applications, Springer Berlin (2014)
104. Tang, T.Y., Daniel, B.K., Romero, C.: on Recommender systems for and insocial and online learning environments. Expert Systems (2014)
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)
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)
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)
108. Vialardi Sacun, C., Bravo Agapito, J., Shafti, L., Ortigosa, A.: Recommendation in Higher Education Using Data Mining Techniques. EDM 2009: 191–199 (2009)
109. Verbert, K., Duval, E., Lindstaedt, S. and Gillet, D. (eds):on Context-aware Recommender Systems, Journal of Universal Computer Science, 16(16), pp. 2175–2290 (2010)
110. Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-Driven Researchto Support Learning and Knowledge Analytics. Educational Technology & Society, 15 (3), 133–148
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)
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)
113. Vuorikari, R., Manouselis, N., and Duval, E.on social information retrieval for technology enhanced learning. Journal Of Digital Information, 10(2) (2009)
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)
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)
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)
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)
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)
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)
120. Weppner, J., Lukowicz, P., Hirth, M., Kuhn, J. Physics education with Google Glass gPhysics experiment app. In Proceedings of the 2014 ACMInternational Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp ‘14 Adjunct), 279–282 (2014)
121. Yu, Z., Zhou, X., Shu, L.: Towards a semantic infrastructure for context-aware e-learning. Multimedia Tools Appl. 47(1): 71–86 (2010)
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)
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.ch00(2012)
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)
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:10.4018/978-1-1350-489-5.ch012 (2012)