Social Semantic Web-Based Analytics Approach to Support Lifelong Learning
The purpose of this paper is to describe how learning analytics approaches based on social semantic web techniques can be applied to enhance the lifelong learning experiences in a connectivist perspective. For this reason, a prototype of a system called SoLearn (Social Learning Environment) that supports this approach. We observed and studied literature related to lifelong learning systems, social semantic web and ontologies, connectivism theory, learning analytics approaches and reviewed implemented systems based on these fields to extract and draw conclusions about necessary features for enhancing the lifelong learning process. The semantic analytics of learning can be used for viewing, studying and analysing the massive data generated by learners, which helps them to understand through recommendations, charts and figures their learning and behaviour, and to detect where they have weaknesses or limitations. This paper emphasises that implementing a learning analytics approach based on social semantic web representations can enhance the learning process. From one hand, the analysis process leverages the meaning expressed by semantics presented in the ontology (relationships between concepts). From the other hand, the analysis process exploits the discovery of new knowledge by means of inferring mechanism of the semantic web.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3298673Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 292
 J. Field, Lifelong learning and the new educational order. Trentham Books, Ltd., Westview House, 734 London Road, Stoke on Trent, ST4 5NP, United Kingdom UK (15.99 British pounds; 25 Euros). 2000.
 J. Kimmerle, J. Moskaliuk, A. Oeberst, and U. Cress, “Learning and collective knowledge construction with social media: A process-oriented perspective”, Educational Psychologist, vol. 50(2), pp. 120-137, 2015.
 S. Downes, Connectivism and connective knowledge: Essays on meaning and learning networks. National Research Council Canada, 2012.
 T. Ley, R. Klamma, S. Lindstaedt, and F. Wild, Learning analytics for workplace and professional learning, In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, ACM, pp. 484-485, April 25 - 29, Edinburgh, United Kingdom, 2016.
 D. Schugurensky, The forms of informal learning: Towards a conceptualization of the field. Centre for the Study of Education and Work, OISE/UT, NALL Working Paper;19, 2000.
 E. Jokisalo, and A. Riu, “Learning in the era of Web2.0”, eLearning Papers, vol. 14, no. 5. 2009.
 G. Bull, A. Thompson, M. Searson, J. Garofalo, J. Park, C. Young and J. Lee, “Connecting Informal and Formal Learning Experiences in the Age of Participatory Media”. Contemporary Issues in Technology and Teacher Education, vol. 8, no. 2, pp. 100-107, 2010.
 S. Ferretti, S. Mirri, L.A. Muratori, M. Roccetti and P. Salomoni. E-learning 2.0: you are We-LCoME! In Proceedings of the 2008 international cross-disciplinary conference on Web accessibility (W4A), Apr 21, 22 2008, ACM, pp. 116-125, Beijing, China, 2008.
 S. G. Mazman and Y. K. Usluel “Modeling educational usage of Facebook”. Computers & Education, Vol. 55 No. 2, pp. 444-453, 2010.
 G. Seimens, “Connectivism: Learning as network creation”. ASTD Learning News, vol. 10, no. 1, 2005.
 R. Ferguson, “Learning analytics: drivers, developments and challenges”, International Journal of Technology Enhanced Learning, vol. 4(5-6), pp. 304-317, 2012.
 Society for Learning Analytics Research (SoLAR) website, https://solaresearch.org. Accessed on 17 January 2019.
 T. Elias, “Learning analytics”. Learning, pp. 1-22, 2011.
 M. Van Harmelen and D. Workman. Analytics for Learning and Teaching. CETIS Analytics Series vol. 1, no. 3, 2012.
 D. Ifenthaler and C. Widanapathirana, “Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines”. Technology, Knowledge and Learning, pp. 1–20, 2014.
 K. Halimi, H. Seridi-Bouchelaghem, and C. Faron-Zucker, “An enhanced personal learning environment using social semantic web technologies”, Interactive Learning Environments, vol. 22(2), pp. 165-187, 2014.
 O. Skrypnyk, S. Joksimović, V. Kovanović, D Gašević and S. Dawson, Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. The International Review of Research in Open and Distributed Learning, vol. 16(3), 2015.
 J. Ahn, What can we learn from Facebook activity? Using social learning analytics to observe new media literacy skills. In. Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK’13), ACM, 8-13 April, pp. 135-144, Leuven, Belgium 2013.
 E. Aguiar, G. A. Ambrose Ambrose, N. V. Chawla, V. Goodrich, and J. Brockman, “Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence”. JLA, vol.1, no.3, pp. 7-33, 2014.
 S. Charleer, J. Klerkx, and E. Duval, Learning dashboards. JLA, vol.1, no.3, pp. 199-202, 2014.
 MeLOD – Mobile Environment for Learning with Linked Open Data website, http://melod.pa.itd.cnr.it. Accessed on 17 January 2019.
 PBL3.0: Integrating Learning Analytics and Semantics in Problem-Based Learning website, http://pbl3-project.eu. Accessed on 17 January 2019.
 MeaningCloud website, https://www.meaningcloud.com. Accessed on 17 January 2019.