REVISIÓN BIBLIOGRÁFICA |
Ally, M. (2004). Foundations of educational theory for online learning. In Theory and practice of online learning (Vol. 2, pp. 15–44). Retrieved from http://cde.athabascau.ca/online_book/ch1.html
Almosallam, E. A., & Ouertani, H. C. (2014). Learning analytics: Definitions, applications and related fields a study for future challenges. In Lecture Notes in Electrical Engineering (Vol. 285 LNEE, pp. 721–730). doi:10.1007/978-981-4585-18-7-81
Anderson, T. (2008). Toward a theory of online learning. In T. Anderson (Ed.), The theory and practice of online learning (pp. 33–60). Athabasca University Press. Retrieved from http://cde.athabascau.ca/online_book/ch2.html
Baker, R. S. J. D. (2010). Data mining for education. International Encyclopedia of Education, 7, 112–118.
Baker, R. S. J. D., & Siemens, G. (2014). Educational Data Mining and Learning Analytics. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences. Cambridge University Press.
Baker, R. S. J. D., & Yacef, K. (2009). The State of Educational Data Mining in 2009 : A Review and Future Visions. Journal of Educational Data Mining, 1(1), 3–16.
Baradwaj, B., & Pal, S. (2012). Mining educational data to analyze student’s performance. Internation Journal Od Advamced Computer Science and Applications, 2(6), 63–69. Retrieved from http://arxiv.org/abs/1201.3417
Bishop, C. M. C. C. M. (2006). Pattern recognition and machine learning. (M. Jordan, J. Kleinberg, & B. Schölkopf, Eds.)Pattern Recognition (Vol. 4, p. 738). Springer. doi:10.1117/1.2819119
Booth, M. (2012). Learning Analytics: The New Black. Educause Review, 47, 52–53. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=trh&AN=79457447&site=ehost-live
Buckingham Shum, S., Hawksey, M., Baker, R. S. J. D., Jeffery, N., Behrens, J. T., & Pea, R. (2013). Educational data scientists. In Proceedings of the Third International Conference on Learning Analytics and Knowledge - LAK ’13 (p. 278). ACM Press. doi:10.1145/2460296.2460355
Chandra, E., Nandhini, K. (2010). Knowledge mining from student data. European Journal of Scientific Research, 47(1), 156–163. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-79954438183&partnerID=40&md5=8226264c617bea82c4f9dcd69e35490b
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5/6), 318. doi:10.1504/IJTEL.2012.051815
Chau, V. T. N., & Phung, N. H. (2012). A knowledge-driven educational decision support system. In 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future, RIVF 2012.
Clow, D. (2012). The learning analytics cycle. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12 (p. 134). New York, New York, USA: ACM Press. doi:10.1145/2330601.2330636
Dağ, F., & Geçer, A. (2009). Relations between online learning and learning styles☆. Procedia - Social and Behavioral Sciences, 1(1), 862–871. doi:10.1016/j.sbspro.2009.01.155
Data, B. (2013). The Potential of Learning Analytics and Big Data. Learning, 5(4), 366–376. Retrieved from http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6302143\npapers3://publication/doi/10.1109/TLT.2012.19
Dejaeger, K., Goethals, F., Giangreco, A., Mola, L., & Baesens, B. (2012). Gaining insight into student satisfaction using comprehensible data mining techniques. European Journal of Operational Research, 218(2), 548–562.
Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506.
Dringus, L. P. (2012). Learning analytics considered harmful. Journal of Asynchronous Learning Network, 16(3), 87–100. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84872358560&partnerID=tZOtx3y1
Ferguson, R. (2012a). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. doi:10.1504/IJTEL.2012.051816
Ferguson, R. (2012b). The state of learning analytics in 2012: a review and future challenges. Technical Report KMI-12-01, 4(March), 18. Retrieved from http://kmi.open.ac.uk/publications/pdf/kmi-12-01.pdf
García, O. A., & Secades, V. A. (2013). Big data and learning analytics: A potential way to optimize elearning technological tools. In Proceedings of the International Conference e-Learning 2013 (pp. 313–317). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84886911408&partnerID=tZOtx3y1
García-Saiz, D., Palazuelos, C., & Zorrilla, M. (2014). Data mining and social network analysis in the educational field: An application for non-expert users. Studies in Computational Intelligence, 524, 411–439. doi:10.1007/978-3-319-02738-8-15
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, 15(3), 42–57.
Guruler, H., Istanbullu, A., & Karahasan, M. (2010). A new student performance analysing system using knowledge discovery in higher educational databases. Computers and Education, 55(1), 247–254.
Jo, I.-H., Kim, D., & Yoon, M. (2014). Analyzing the log patterns of adult learners in LMS using learning analytics. In Proceedins of the Fourth International Conference on Learning Analytics And Knowledge - LAK ’14 (pp. 183–187). New York, New York, USA: ACM Press. doi:10.1145/2567574.2567616
Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61–72.
Kop, R. (2011). The challenges to connectivist learning on open online networks: Learning experiences during a massive open online course. International Review of Research in Open and Distance Learning, 12(3), 19–38.
KOTSIANTIS, S., PIERRAKEAS, C., & PINTELAS, P. (2004). PREDICTING STUDENTS’ PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES. Applied Artificial Intelligence, 18(5), 411–426. doi:10.1080/08839510490442058
Lin, S. (2012). Data mining for student retention management. Journal of Computing Sciences in Colleges, 27(4), 92–99. Retrieved from http://dl.acm.org/citation.cfm?id=2167450
Mohamad, S. K., & Tasir, Z. (2013). Educational Data Mining: A Review. Procedia - Social and Behavioral Sciences, 97, 320–324. doi:10.1016/j.sbspro.2013.10.240
Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-Learning, online learning, and distance learning environments: Are they the same? The Internet and Higher Education. doi:10.1016/j.iheduc.2010.10.001
Natek, S., & Zwilling, M. (2014). Student data mining solution-knowledge management system related to higher education institutions. Expert Systems with Applications, 41(14), 6400–6407.
Picciano, Anthony G.1, 2. (2012). THE EVOLUTION OF BIG DATA AND LEARNING ANALYTICS IN AMERICAN HIGHER EDUCATION. Journal of Asynchronous Learning Networks, 16, 9–20.
Piety, P. J., Hickey, D. T., & Bishop, M. J. (2014). Educational data sciences. In Proceedins of the Fourth International Conference on Learning Analytics And Knowledge - LAK ’14 (pp. 193–202). New York, New York, USA: ACM Press. doi:10.1145/2567574.2567582
Romero, C., & 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). doi:10.1109/TSMCC.2010.2053532
Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. doi:10.1002/widm.1075
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368–384. doi:10.1016/j.compedu.2007.05.016
Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Leony, D., & Delgado Kloos, C. (2014). ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. Computers in Human Behavior.
Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends® in Machine Learning.
Shen, D., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Education, 19, 10–17.
Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Educational Technology and Society, 15(3), 3–26. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84873838796&partnerID=tZOtx3y1
Siemens, G. (2012). Learning analytics. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12 (p. 4). New York, New York, USA: ACM Press. doi:10.1145/2330601.2330605
Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380–1400. Retrieved from http://usyd.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwVZ07DsIwDIYjxAmQQIy9QKXaiZN0RlQcAA4QP9KN-484EgPMXrz48f2S_YcwJZA4NlHUGDsJq2WmKmCiQF3lT8b-6ebbKRzsfQ6v7f68PeavGcC840JetTUTGyTutUQhFQBGB3aMvS7GideVWvZ8ohE3xTbs8kQ7Z0VGUriEowO1XcNUpXg8-yztMt6L1aaONblo4jQORT_DKTAY
Siemens, G., & Baker, R. S. J. (2012a). Learning Analytics and Educational Data Mining : Towards Communication and Collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252–254). doi:10.1145/2330601.2330661
Siemens, G., & Baker, R. S. J. d. (2012b). Learning analytics and educational data mining. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12 (p. 252). New York, New York, USA: ACM Press. doi:10.1145/2330601.2330661
Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1510–1529. doi:10.1177/0002764213479366
Tair, M. M. A., & El-halees, A. M. (2012). Mining Educational Data to Improve Students ’ Performance : A Case Study. International Journal of Information and Communication Technology Research, 2(2), 140–146.
The Mendeley Support Team, Siemens, G., Long, P., Gasevic, D., Haythornthwaite, C., Dawson, S., … Becker, B. (2011). Developing a grounded theory approach: a comparison of Glaser and Strauss. International Journal of Technology Enhanced Learning, 4, 1–4. doi:10.1145/2330601.2330605
Winne, P. H., & Baker, R. S. J. d. (2013). The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning. JEDM - Journal of Educational Data Mining. Retrieved from http://www.educationaldatamining.org/JEDM/index.php/JEDM/article/view/28
Yang, T., Jin, R., & Mahdavi, M. (2011). Regret Bound by Variation for Online Convex Optimization. arXiv Preprint arXiv:1111.6337, 1–18. Retrieved from http://arxiv.org/abs/1111.6337
Yeh, Y. C. (2010). Analyzing online behaviors, roles, and learning communities via online discussions. Educational Technology and Society, 13(1), 140–151.
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