Abstract
The performed analysis of innovative learning analytics systems clearly shows that in the near future those systems will be actively deployed by academic institutions. The on-going research project described here is focused on in-depth analysis of hierarchical levels of learning analytics and academic analytics, types of data to be collected, main features, and the conceptual design of smart learning analytics for smart university. Our vision is that modern analytics systems should strongly support smart university’s “smartness” levels such as adaptivity, sensing, inferring, anticipation, self-learning, and self-organization. This paper presents the up-to-date research outcomes of a research project on the design and development of smart learning analytics systems for smart universities.
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References
Uskov, V.L, Bakken, J.P., Pandey, A., Singh, U., Yalamanchili, M., Penumatsa, A.: Smart university taxonomy: features, components, systems. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2016, pp. 3–14. Springer, June 2016. 643 p., ISBN: 978-3-319-39689-7
Uskov, V., Bakken, J., Pandey, A. The ontology of next generation smart classrooms, In: Uskov, et al. (eds.) Smart Education and Smart e-Learning, pp. 3–14. Springer (2015). 510 p., ISBN 978-3-319-19874-3
Uskov, A., Sekar, B.: Smart gamification and smart serious games. In: Sharma, D., Jain, L., Favorskaya, M., Howlett, R. (eds.) Fusion of Smart, Multimedia and Computer Gaming Technologies, Intelligent Systems Reference Library, vol. 84, pp. 7–36. Springer (2015). doi:10.1007/978-3-319-14645-4_2, ISBN: 978-3-319-14644-7
Society for Learning Analytics Research. https://solaresearch.org/
Friesen, N.: Learning analytics: readiness and rewards. Can. J. Learn. Technol. 39(4), 1–12 (2013). http://learningspaces.org/wordpress/wp-content/uploads/2013/05/Learning-Analytics1.pdf
Siemens, G.: The journal of learning analytics: supporting and promoting learning analytics research. J. Learn. Analyt. 1(1), 3–4 (2014)
Suchithra, R., Vaidhehi, V., Iyer, N.E.: Survey of learning analytics based on purpose and techniques for improving student performance. Int. J. Comput. Appl. 111(1), 22–26 (2015)
Borkar, S., Rajeswari, K.: Attributes selection for predicting students’ academic performance using education data mining and artificial neural network. Int. J. Comput. Appl. 86(10), 25–29 (2014)
Tempelaar, D.T., Cuypers, H., van de Vrie, E., Heck, A., van der Kooij, H.: Formative assessment and learning analytics. In: Proceedings of the 2013 International Conference on Learning Analytics and Knowledge (LAK), 8–12 April 2013, Leuven, Belgium (2013). https://pdfs.semanticscholar.org/29db/489b4532ee7e983a17f5653d65b31e9cb93d.pdf
Suchithra, R., Vaidhehi, V., Iyer, N.E.: Survey of learning analytics based on purpose and techniques for improving student performance. Int. J. Comput. Appl. 111, 22–26 (2015). ISSN: 0975–8887
Mattingly, K.D., Rice, M.C.: Learning Analytics as a Tool for Closing the Assessment Loop in Higher Education. Knowl. Manag. E-Learning: An Int. J. 4(3), 236–247 (2012). http://kmel-journal.org/ojs/index.php/online-publication/article/viewFile/196/148
Siemens, G., Dawson, S., Lynch, G.: Improving the quality and productivity of the higher education sector, Society for Learning Analytics Research (2013). https://sydney.edu.au/education-portfolio/ei/projects/SoLAR_Report_2014.pdf
Dietz-Uhler, B., Hurn, J.E.: Using learning analytics to predict (and improve) student success: a faculty perspective. J. Interact. Online Learn. 12(1), 17–26 (2013). Spring. ISSN: 1541-4914. www.ncolr.org/jiol
Dyckhoff, A.L., Zielke, D., Bültmann, M., Chatti, M.A., Schroeder, U.: Design and implementation of a learning analytics toolkit for teachers. Educ. Technol. Soc. 15(3), 58–76 (2012)
Ruiperez-Valiente, J.A., Munoz Merino, P.J., Delgado-Kloos, C.: An architecture for extending the learning analytics support in the Khan Academy framework. In: Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality, Salamanca, Spain, pp. 277–284. ACM (2013). doi:10.1145/2536536.2536578
Siemens, G., Gasevic, D., Haythornthwaite. C., Dawson, S., Buckingham Shum, S., Ferguson, R., Duval, E., Verbert, K., Baker, R.S.J.d.: Open Learning Analytics: an integrated & modularized platform (2011). http://www.elearnspace.org/blog/wp-content/uploads/2016/02/ProposalLearningAnalyticsModel_SoLAR.pdf
Giannakos, M.N., Sampson, D.G., Kidziński, L.: Introduction to smart learning analytics: foundations and developments in video-based learning. Smart Learn. Environ. 3(12) (2016). https://slejournal.springeropen.com/articles/10.1186/s40561-016-0034-2
Boulanger, D., Seanosky, J., Kumar, V., Kinshuk, Panneerselvam, K., Somasundaram, T.S.: Smart learning analytics. In: Chen, G., Kumar, V., Kinshuk, Huang, R., Kong, S. (eds.) Emerging Issues in Smart Learning. Lecture Notes in Educational Technology. Springer, Heidelberg (2015)
Smart Learning Analytics project at Bradley University – References. www.interlabs.bradleu.edu/SLA_project
Ogor, E.: Student academic performance monitoring and evaluation using data mining techniques. In: Electronics, Robotics and Automotive Mechanics Conference. IEEE (2007)
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L.: Learning Analytics Dashboard Applications. SAGE Publications (2013)
Jenny McDonald, et al.: Cross-institutional collaboration to support student engagement: SRES version 2 (2016)
Eleni Koulocheri, et al.: Applying learning analytics in an open personal learning environment: A quantitative approach. IEEE (2012)
Martinez-Maldonado, R., Pardo, A., Mirriahi, N., Yacef, K., Kay, J., Clayphan, A.: LATUX: an iterative workflow for designing validating, and deploying learning analytics visualizations. J. Learn. Analyt. 2(3), 9–39 (2015)
Arnold, K.E., Pistilli, M.D.: Course signals at purdue: using learning analytics to increase student success. In: Proceedings of the Second International Conference on Learning Analytics and Knowledge LAK 2012, Vancouver, BC, Canada. ACM (2012). 978-1-4503-1111-3/12/04
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Uskov, V.L. et al. (2018). Building Smart Learning Analytics System for Smart University. In: Uskov, V., Howlett, R., Jain, L. (eds) Smart Education and e-Learning 2017. SEEL 2017. Smart Innovation, Systems and Technologies, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-319-59451-4_19
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