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Deploying ad-hoc learning environments to use and represent data from multiple sources and networks and to dynamically respond to user demands could be very expensive and ineffective in the long run. Moreover, most of the available data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards. It is preferable to focus on data availability to choose and develop interoperability strategies suitable for smart learning systems based on open standards and allowing seamless integration of third-party data and custom applications. This paper highlights the opportunity to take advantage of emerging technologies, like the linked open data platforms and automatic reasoning to effectively handle the vast amount of information and to use data linked queries in the domain of cognitive smart learning systems.
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- Andronico, A., Carbonaro, A., Colazzo, L., Molinari, A., Ronchetti, M., & Trifonova, A. (2004) Designing models and services for learning systems in mobile settings, Mobile and Ubiquitous Information Access Workshop 2003, LNCS 2954, Springer-Verlag: Berlin Heidelberg, 90-106.
- Bizer C., Heath, T., Berners-Lee (2011). Linked data: The story so far. In Semantic services, interoperability and web applications: emerging concepts. IGI Global, 205–227.
- Carbonaro A, F. Piccinini, R. Reda (2018) Integrating heterogeneous data of healthcare devices to enable domain data management. Journal of E-Learning and Knowledge Society 14(1).
- Carbonaro A. (2010) Improving web search and navigation using summarization process, Communications in Computer and Information Science Volume 111 CCIS, Issue PART 1, 131-138.
- Carbonaro A. (2010) WordNet-based summarization to enhance learning interaction tutoring. Journal of e-Learning and Knowledge Society 6(2), 67–74.
- Carbonaro A. (2012) Interlinking e-learning resources and the web of data for improving student experience, Journal of E-Learning and Knowledge Society, 8(2), 33-44.
- Carbonaro A., M. Ravaioli, (2017) Peer assessment to promote Deep Learning and to reduce a Gender Gap in the Traditional Introductory Programming Course, Journal of e-Learning and Knowledge Society, 3(13).
- Chrysafiadi K., Virvou M., Sakkopoulos E. (2020) Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment. In: Virvou M., Alepis E., Tsihrintzis G., Jain L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 158. Springer, Cham.
- Coccoli, M., Maresca, P., and Stanganelli, L. (2016). Cognitive computing in education. Journal of E-Learning and Knowledge Society, 12(2), 55-69.
- Coccoli, M., Maresca, P., and Stanganelli, L. (2017). The role of big data and cognitive computing in the learning process. Journal of Visual Languages & Computing, 38, 97-103.
- Gomede, E., Gaffo, F. H., Briganó, G. U., de Barros, R. M., & Mendes, L. D. S. (2018). Application of Computational Intelligence to Improve Educationin Smart Cities.Sensors,18(1).
- Harrison C., B. Eckman, R. Hamilton (2010) Foundations for smarter cities, IBM Journal of Research and Development, vol. 54, no. 4, 1–16.
- Lim C., Kim, K.-J., P.P. Maglio (2018) Smart cities with big data: reference models, challenges, and considerations. Cities 82, 86–99.
- Muniasamy, Anandhavalli; Alasiry, Areej. (2020) Deep Learning: The Impact on Future eLearning. International Journal of Emerging Technologies in Learning, ISSN 1863-0383, 15(1), 188-199.
- Patel A. and S. Jain (2019) Present and future of semantic web technologies: A research statements, International journal of computers and applications, Talyor and Francis.
- Pereira, K. C., Wolfgand Matsui Siqueira, S., Pereira B. Nunes, S. Dietze (2018) Linked data in Education: a survey and a synthesis of actual research and future challenges, IEEE Transactions on Learning Technologies, 11(3), 400-412.
- Piedra N., Chicaiza, J., Lopez, J., E. Tovar (2015). Seeking open educational resources to compose massive open online courses in engineering education an approach based on Linked Open Data. Journal of Universal Computer Science, 21(5), 679–711.
- Qing, H., Dietze, S., Giordano, D., Taibi, D., Kaldoudi, E., and N. Dovrolis (2012) The Open University’s repository of research publications Linked education: interlinking educational resources and the web of data. in The 27th ACM Symposium On Applied Computing, Special Track on Semantic Web and Applications.
- Reda R., F. Piccinini, A. Carbonaro (2018) Towards consistent data representation in the IoT healthcare landscape. In ACM DH’18: International Digital Health Conference, France.
- Riccucci S., A. Carbonaro, G. Casadei (2007) Knowledge acquisition in intelligent tutoring system: A data mining approach. In Mexican International Conference on Artificial Intelligence. Springer, 1195–1205.
- Riccucci, S., Carbonaro, A., Casadei, G. (2005) An architecture for knowledge management in intelligent tutoring systems. In: Proceedings of IADIS International Congress on Cognition and Exploratory Learning in Digital Age, 2005, 473-476.
- Ristoski P., H. Paulheim (2016) Semantic Web in data mining and knowledge discovery: A comprehensive survey, Web Semantics: Science, Services and Agents on the World Wide Web. vol. 36.
- Vega-Gorgojo, G., Asensio-Perez, J.I., E. Gomez-Sanchez (2015). A review of Linked Data proposals in the learning domain. Journal of Universal Computer Science, 21(2), 326–364.