Big data for social media learning analytics: potentials and challenges ARTICLE
Stefania Manca, Institute of Educational Technology, National Research Council of Italy ; Luca Caviglione, Institute for Intelligent Systems for Automation, National Research Council of Italy ; Juliana Raffaghelli
Journal of e-Learning and Knowledge Society Volume 12, Number 2, ISSN 1826-6223 e-ISSN 1826-6223 Publisher: Italian e-Learning Association
Today, the information gathered from massive learning platforms and social media sites allow deriving a very comprehensive set of learning information. To this aim, data mining techniques can surely help to gain proper insights, personalize learning experiences, formative assessments, performance measurements, as well as to develop new learning and instructional design models. Therefore, a core requirement is to classify, mix, filter and process the involved big data sources by means of proper learning and social learning analytics tools. In this perspective, the paper investigates the most promising applications and issues of big data for the design of the next-generation of massive learning platforms and social media sites. Specifically, it addresses the methodological tools and instruments for social learning analytics, pitfalls arising from the usage of open datasets, and privacy and security aspects. The paper also provides future research directions.
Manca, S., Caviglione, L. & Raffaghelli, J. (2016). Big data for social media learning analytics: potentials and challenges. Journal of e-Learning and Knowledge Society, 12(2),. Italian e-Learning Association. Retrieved August 21, 2018 from https://www.learntechlib.org/p/173464/.
- Anshari, M., Alas, Y., Hj Mohd Yunus, N., Pg Hj Sabtu, N., & Sheikh Abdul Hamid, M. (2016), Online Learning: trends, issues and challenges in the Big Data Era. Journal of E-Learning and Knowledge Society, 12(1), 121-134.
- Atenas, J., Havemann, L., & Priego, E. (2015), Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship. Open Praxis, 7(4), 377-389.
- Borgman, C. (2015), Big data, Little data, No data: Scholarship in the Networked World. Cambridge, MA: The MIT Press.
- Buckingham Shum, S., & Ferguson, R. (2012), Social learning analytics. Educational Technology& Society, 15(3), 3-26.
- Caviglione, L. (2009), Can satellites face trends? The case of Web 2.0. In Proceedings of International Workshop on Satellite and Space Communications, 446-450.
- Caviglione, L., & Coccoli, M. (2011), Privacy problems with Web 2.0. Computer Fraud and Security, 10, 16-19.
- Caviglione, L., Coccoli, M., & Gianuzzi, V. (2011), Opportunities, integration and issues of applying new technologies over e-learning platforms. In Proceedings of the 3rd International Conference on Next Generation Networks and Services (NGNS 2011), 12-17.
- Caviglione, L., Coccoli, M., & Merlo, A. (2014), A taxonomy-based model of security and privacy in online social networks. International Journal of Computational Science and Engineering, 9(4), 325-338.
- Chanier, T. & Reffay, C. (2011), Rapport Final du Project MULCE. URL: http://mulcedoc.univ-bpclermont.fr/IMG/pdf/rapport_fin_de_projet_ANR_Mulce_110317.pdf
- Coccoli, M., Guercio, A., Maresca. P., & Stanganelli, L. (2014), Smarter Universities: a Vision for the Fast Changing Digital Era. Journal of Visual Languages and Computing, 25(6), 1003-1011.
- Cooper, S., & Sahami, M. (2013), Reflections on Stanford’s MOOCs. Communications of the ACM, 56(2), 28-30.
- Daries, J.P., Reich, J., Waldo, J., Young, E., Whittinghill, J., Seaton, D.T., Ho, A.D., & Chuang, I. (2014), Privacy, Anonymity, and Big Data in the Social Sciences. Queue – Practice, 12(7), 1-30.
- Esposito, A. (2012), Research ethics in emerging forms of online learning: issues arising from a hypothetical study on a MOOC. The Electronic Journal of e-Learning, 10(3), 315-325.
- Eynon, R. (2013). (2013), The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology, 38(3), 237-240.
- Ferguson, R., & Clow, D. (2015), Consistent commitment: Patterns of engagement across time in Massive Open Online Courses (MOOCs). Journal of Learning Analytics, 2(3), 55–80.
- Jändel, M. (2014), Decision support for releasing anonymised data. Computers& Security, 46, 48-61.
- Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2015), NMC Horizon Report: 2015 Higher Education Edition. Austin, TX: The New Media Consortium.
- Joksimović, S., Hatala, M., & Gašević, D. (2014), Learning Analytics for Networked Learning Models. Journal of Learning Analytics, 1(3), 191–194.
- Kop, R. (2011), The Challenges to Connectivist Learning on Open Online Networks: Learning Experiences during a Massive Open Online Course. The International Review of Research in Open and Distance Learning, 12(3), 19-38.
- Long, P., & Siemens, G. (2011), Penetrating the Analytics in Learning and Education. EDUCASE review, September/October 2011, 31-40.
- Mazurczyk, W., & Caviglione, L. (2014), Steganography in Modern Smartphones and Mitigation Techniques. IEEE Communications Surveys& Tutorials, 17(1), 334-357.
- Merceron, A., Blikstein, P., & Siemens, G. (2015), Learning Analytics: From Big Data to Meaningful Data. Journal of Learning Analytics, 2(3), 4-8.
- Selwyn, N. (2015), Data entry: towards the critical study of digital data and education. Learning, Media and Technology, 40(1), 64-82.
- Siemens, G. (2005), Connectivism: Learning as network creation. ElearnSpace. URL: http://www.elearnspace.org/Articles/networks.htm (accessed on 4th March 2016).
- Siemens, G. (2013), Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
- Slade, S., & Prinsloo, P. (2013), Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1510–1529.
- Tufekci, Z. (2014), Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. In Proceedings of the Eight International AAAI Conference on Weblogs and Social Media, 505-514.
- 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.
These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact firstname.lastname@example.org.
Dodzi Amemado & Stefania Manca
Journal of e-Learning and Knowledge Society Vol. 13, No. 2 (May 29, 2017)
These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact email@example.com.