References
- [1] Hämäläinen, W., Laine, T., and Sutinen, E., “Data mining in personalizing distance education courses”, in Editor (Ed.)^(Eds.): “Book Data mining in personalizing distance education courses” (Citeseer, 2004, edn.), pp.
- [2] Dey, A.K., “Understanding and Using Context”, Personal Ubiquitous Comput., 2001, vol. 5, (1), pp. 4–7. http://dx.doi.org/10.1007/s007790170019
- [3] Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., and Duval, E. “Context-aware recommender systems for learning: a survey and future challenges”, IEEE Transactions on Learning Technologies, 2012, vol. 5, (4), pp. 318–335. http://dx.doi.org/10.1109/TLT.2012.11
- [4] Anohina, A., Lavendelis, E., and Grundspenkis, J. “The Concept Map-Based Knowledge Assessment System with Reduction of Task Difficulty,” Information Systems Development, 2009, pp. 1–13.
- [5] Lonsdale, P., Baber, C., Sharples, M., Byrne, W., Arvanitis, T.N., Brundell, P., and Beale, R. “Context awareness for MOBIlearn: creating an engaging learning experience in an art museum”, Mobile learning anytime everywhere, 2005, 115.
- [6] Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D., “Context Aware Computing for The Internet of Things: A Survey”, 2013
- [7] Lavendelis, E. “Open multi-agent architecture and methodology for intelligent tutoring system development,” Summary of Doctoral Thesis, 2009, pp. 49.
- [8] Devedzic, V., Debenham, J., and Popovic, D. “Teaching formal languages by an intelligent tutoring system,” Educ Tech Soc, 2000, vol. 3, (2), pp. 36–49.
- [9] Kotsiantis, S., Pierrakeas, C., and Pintelas, P. “Preventing student dropout in distance learning using machine learning techniques”, in Editor (Ed.)^(Eds.): “Book Preventing student dropout in distance learning using machine learning techniques” (Springer, 2003, edn.), pp. 267–274.
- [10] Zhang, Q., Cheng, L., and Boutaba, R.: “Cloud computing: state-of-theart and research challenges,” Journal of internet services and applications, 2010, vol. 1, (1), pp. 7–18. http://dx.doi.org/10.1007/s13174-010-0007-6
- [11] Kranjc, J., Podpečan, V., and Lavrač, N. “Clowdflows: a cloud based scientific workflow platform”: “Machine Learning and Knowledge Discovery in Databases” (Springer, 2012), pp. 816–819.
- [12] Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., and Hellerstein, J.M. “Distributed GraphLab: a framework for machine learning and data mining in the cloud,” in Proceedings of the VLDB Endowment, 2012, vol. 5, (8), pp. 716–727. http://dx.doi.org/10.14778/2212351.2212354
- [13] Neven, F., and Duval, E., “Reusable learning objects: a survey of LOM-based repositories,” in Editor (Ed.)^(Eds.): “Book Reusable learning objects: a survey of LOM-based repositories” (ACM, 2002, edn.), pp. 291–294.
- [14] Bellavista, P., Corradi, A., Fanelli, M., and Foschini, L., “A survey of context data distribution for mobile ubiquitous systems,” ACM Computing Surveys (CSUR), 2012, vol. 44, (4), pp. 24. http://dx.doi.org/10.1145/2333112.2333119
- [15] Gómez, S., Zervas, P., Sampson, D.G., and Fabregat, R. “Context-aware adaptive and personalized mobile learning delivery supported by UoLmP,” Journal of King Saud University-Computer and Information Sciences, 2014, vol. 26, no. 1, pp. 47–61. http://dx.doi.org/10.1016/j.jksuci.2013.10.008
- [16] Rikure, T., “Development of learner psycho-physiological model based adaptive tutoring systems,” Summary of doctoral thesis, 2009
- [17] Russell, S.J., and Norvig, P., Artificial intelligence: a modern approach, Prentice hall Englewood Cliffs, 2010.