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Resumen
Esta parte realiza un aporte desde el concepto de learning analytics y las acciones según el trabajo con modelos con grandes cantidades de datos, mostrando la tendencia de crecimiento y auge en la actualidad de esta área de estudios en bases de indexación bibliográfica, número de autores y número de eventos académicos realizados sobre este tema.
Referencias
Baker, R. S. e Inventado, P. S. (2014). Educational data mining and learning analytics. En J. Larusson y B. White (eds.), Learning analytics. Nueva York: Springer.
Baker, R. S. J. d. (s. f.). Educational data mining and learning analytics. Recuperado de Recuperado de http://www.columbia.edu/~rsb2162/BakerSiemensHandbook2013.pdf
Bhadane, C., Dalal, H. y Doshi, H. (2015). Sentiment analysis: Measuring opinions. Procedia Computer Science, 45, 808-814. doi:10.1016/j.procs.2015.03.159
Calvet Liñán, L. y Juan Pérez, Á. A. (2015). Educational Data mining and learning analytics: Differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education, 12(3), 98-112. https://doi.org/10.7238/rusc.v12i3.2515
Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S. y Zhou, X. (2013). Big data challenge: A data management perspective. Frontiers of Computer Science, 7(2), 157-164.
Chen, H., Chiang, R. H. y Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. Recuperado de https://goo.gl/ujFhG3
Chorianopoulos, K., Giannakos, M. N., Chrisochoides, N. y Reed, S. (2014). Open service for video learning analytics. En Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on (pp. 28-30). Atenas: IEEE. 10.1109/ICALT.2014.19
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695. Recuperado de http://www.tandfonline.com/doi/abs/10.1080/13562517.2013.827653
Conway, D. (2010). The Data Science Venn Diagram. Recuperado de http://www.dataists.com/2010/09/the-data-science-venn-diagram/
Dhar, V. (2012). Data science and prediction. Communications of the ACM, 56(12), 64-73. doi:10.2139/ssrn.2086734
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304-317. doi:10.1504/IJTEL.2012.051816
Garćia-Peñalvo, F. J. y Safont, L. V. (2014). Human behaviors in computer-based education systems. Computers in Human Behavior, 31(1), 432-433.
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D. … y Van Alstyne, M. (2009). Social science: Computational social science. Science, 323(5915), 721-723.
Mohamad, S. K. y Tasir, Z. (2013). Educational data mining: A review. Procedia-Social and Behavioral Sciences, 97, 320-324. https://doi.org/10.1016/j.sbspro.2013.10.240
Romero, C. y Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618. 10.1109/TSMCC.2010.2053532
Sawyer, R. (2006). The new science of learning. En R. K. Sawyer (ed.), The Cambridge handbook of the learning sciences (pp. 1-16). Cambridge University Press.
Shum, S. B. y Ferguson, R. (2012). Social learning analytics. Educational Technology and Society, 15(3), 3-26. Recuperado de http://www.scopus.com/inward/record.url?eid=2-s2.0-84873838796ypartnerID=tZOtx3y1
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. Recuperado de http://journals.sagepub.com/doi/abs/10.1177/0002764213498851
Thomas, J. J. y Cook, K. A. (2006). A visual analytics agenda. IEEE Computer Graphics and Applications, 26(1), 10-13. 10.1109/MCG.2006.5
Wu, X., Zhu, X., Wu, G.-Q. y Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107. doi:10.1109/TKDE.2013.109
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