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DATOS DEL INVESTIGADOR PRINCIPAL
Nombre ALVARO QUIROGA
Nombre del perfíl Obervatorio de Educación
Grupo de investigación FICB-IUPG
Línea de investigación Línea De Investigación En Educación Y Tecnología
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TÍTULO DEL PROYECTO LEARNING ANALYTICS – ANÁLISIS DE LOGRO ACADÉMICO - ESTUDIO DE CASO
PALABRAS CLAVE LEARNING ANALYTICS, EDUCATIONAL DATA MINING
OBJETIVOS DEL PROYECTO Identificar patrones de comportamiento de los estudiantes en la plataforma de aprendizaje de los programas de educación virtual del POLITÉCNICO GRANCOLOMBIANO durante el primer semestre de 2014.
PERTINENCIA ESPISTEMOLÓGICA DEL PROYECTO El estudio de comportamiento de los estudiantes permite conocer cómo se lleva a cabo el proceso de educación en una institución. Este conocimiento facilita determinar acciones para que los estudiantes culminen con éxito sus estudios.
RELEVANCIA DEL PROYECTO PARA LA INSTITUCIÓN Y PARA LOS BENEFICIARIOS DEL PROYECTO Hoy la educación virtual es una tendencia. Todo el conocimiento que se genere sobre métodos que permitan que el estudiante culmine sus estudios con éxito es valioso para la sociedad dada la importancia de la educación misma.
PROBLEMA DE INVESTIGACIÓN Identificar los patrones de comportamiento de los estudiantes de educación virtual y relacionarlos con su rendimiento académico.
METODOLOGÍA Uso de herramientas de minería de datos de SQLSERVER sobre la información disponible en las plataformas de educación virtual.
RESULTADOS ESPERADOS Identificar patrones de comportamiento de los estudiantes en la plataforma de educación virtual y su relación con el rendimiento académico.
DURACIÓN DEL PROYECTO 6
POSIBLES FUENTES DE FINANCIACIÓN EXTERNA test
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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
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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.
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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
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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.
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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
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ENTREGABLES
PRODUCTOLUGAR DE DIVULGACIÓNAUTORESBENEFICIARIOSDESCRIPCIÓN
DivulgaciónALVARO QUIROGAPOLITÉCNICO GRANCOLOMBIANOEstado del arte de Learning Analytics
Nuevo Conocimiento ó I+DALVARO QUIROGAPOLITÉCNICO GRANCOLOMBIANOInforme de identificación de patrones de comportamiento de los estudiantes de educación virtual.
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CRONOGRAMA
TIPO DESCRIPCIÓN F.INICIO F.FINAL
Actividad Revisión bibliográfica 1-feb-2015 1-jun-2015
Actividad Recolección de información 1-feb-2015 1-mar-2015
Actividad Análisis de información 1-mar-2015 15-mar-2015
Entregable Presentación de estado del arte, metodología y resultados del estudio 1-jul-2015 15-jul-2015
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PEDIDO DE BIBLIOGRAFÍA
AUTOR TÍTULO EDITORIAL
ANEXOS