Análisis del aprendizaje: una revisión sistemática de literatura.
Titulo:

Análisis del aprendizaje: una revisión sistemática de literatura.
.

Sumario:

La mayoría de algoritmos utilizados en el análisis de datos están diseñados de acuerdo con las capacidades de potencia y flexibilidad más que por su sencillez, y son demasiado complejos de utilizar en el contexto educativo. El objetivo de este trabajo es presentar una revisión de literatura sobre el análisis del aprendizaje en la educación superior: problemas, limitaciones, técnicas y herramientas empleadas. Se utilizó la metodología de la revisión sistemática de literatura para responder a tres preguntas de investigación tomando como base publicaciones científicas. Se concluye que se deben implementar, adaptar o desarrollar algoritmos predeterminados para el contexto educativo y, también, construir herramientas para el análisis de datos ed... Ver más

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Cuaderno activa

2027-8101

2619-5232

Gil Vera, Victor Daniel

TECNOLOGICO DE ANTIOQUIA INSTITUCION UNIVERSITARIA

10.53995/20278101.489

10

2018-04-15

15

26

Colombia

análisis del aprendizaje

análisis del aprendizaje multimodal

intercambio de datos

aprender a aprender

aprendizaje permanente.

Victor Daniel Gil Vera - 2018

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

info:eu-repo/semantics/openAccess

http://purl.org/coar/access_right/c_abf2

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spelling Análisis del aprendizaje: una revisión sistemática de literatura.
Learning analytics: a systematic literature review.
La mayoría de algoritmos utilizados en el análisis de datos están diseñados de acuerdo con las capacidades de potencia y flexibilidad más que por su sencillez, y son demasiado complejos de utilizar en el contexto educativo. El objetivo de este trabajo es presentar una revisión de literatura sobre el análisis del aprendizaje en la educación superior: problemas, limitaciones, técnicas y herramientas empleadas. Se utilizó la metodología de la revisión sistemática de literatura para responder a tres preguntas de investigación tomando como base publicaciones científicas. Se concluye que se deben implementar, adaptar o desarrollar algoritmos predeterminados para el contexto educativo y, también, construir herramientas para el análisis de datos educacionales que cuenten con interfaces intuitivas y fáciles de utilizar.
The majority of algorithms used in the data analysis are designed according to the capacities of power and flexibility rather than for its simplicity and are too complex to use in the educational context. The objective of this paperis to present a literature review on the learning analytics in higher education: problems, limitations, techniques and tools used. The systematicliterature review methodology was used to answer three research questions on the basis of scientific publications. This paper concludes that it mustimplement, adapt or develop algorithms for the educational context and must be build tools for the analysis of educational data with intuitive interfaces and easy to use.
Gil Vera, Victor Daniel
learning analytics
learning to learn
lifelong learning
multimodal learning analytics
data exchange.
análisis del aprendizaje
análisis del aprendizaje multimodal
intercambio de datos
aprender a aprender
aprendizaje permanente.
10
1
Artículo de revista
Journal article
2018-04-15T00:00:00Z
2018-04-15T00:00:00Z
2018-04-15
application/pdf
text/xml
Tecnológico de Antioquia - Institución Universitaria
Cuaderno activa
2027-8101
2619-5232
https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/view/489
10.53995/20278101.489
https://doi.org/10.53995/20278101.489
spa
https://creativecommons.org/licenses/by-nc-sa/4.0
Victor Daniel Gil Vera - 2018
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
15
26
Aguiar, E., Chawla, N. V, Brockman, J., Ambrose, G. A., y Goodrich, V. (2014). Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 103-112). ACM.
Asif, R., Merceron, A., y Pathan, M. K. (2015). Investigating performance of students: a longitudinal study. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 108-112). ACM.
Cambruzzi, W. L., Rigo, S. J., y Barbosa, J. L. V. (2015). Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach. J. UCS, 21(1), 23-47.
Clow, D. (2014). Data wranglers: human interpreters to help close the feedback loop. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 49-53). ACM.
Corrigan, O., Smeaton, A. F., Glynn, M., y Smyth, S. (2015). Using Educational Analytics to Improve Test Performance. In Design for Teaching and Learning in a Networked World (pp. 42-55). Springer.
Diebold, F. (2001). Elements of forecasting. (South y W. C. Publishing., Eds.) (2nd ed.). Australia.
Drachsler, H., y Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 120- 129). ACM.
Friedman, V. (2008). Data visualization and infographics. Graphics, Monday Inspiration, 14, 2008.
Gasevic, D., Kovanovic, V., Joksimovic, S., y Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distributed Learning, 15(5).
Gibson, A., Kitto, K., y Willis, J. (2014). A cognitive processing framework for learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 212-216). ACM.
Grann, J., y Bushway, D. (2014). Competency map: Visualizing student learning to promote student success. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 168-172). ACM.
Grau-Valldosera, J., y Minguillón, J. (2011). Redefining dropping out in online higher education: a case study from the UOC. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 75-80). ACM.
Harrison, S., Villano, R., Lynch, G., y Chen, G. (2015). Likelihood analysis of student enrollment outcomes using learning environment variables: A case study approach. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 141-145). ACM.
Hecking, T., Ziebarth, S., y Hoppe, H. U. (2014). Analysis of dynamic resource access patterns in a blended learning course. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 173-182). ACM.
Holman, C., Aguilar, S., y Fishman, B. (2013). GradeCraft: What can we learn from a gameinspired learning management system? In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 260-264). ACM.
KDnuggets. (2017). Data Science Statistics 101. Retrieved June 3, 2017, from http://www.kdnuggets.com/2016/07/data-sciencestatistics-101.html
KDNuggets. (2017a). Association Rules and the Apriori Algorithm: A Tutorial. Retrieved March 7, 2016, from http://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html
KDNuggets. (2017b). Comparing Clustering Techniques: A Concise Technical Overview. Retrieved June 3, 2017, from http://www.kdnuggets.com/2016/09/comparing-clusteringtechniques-concise-technical-overview.html
KDNuggets. (2017c). Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results. Retrieved March 6, 2017, from http://www.kdnuggets.com/2016/02/ensemble-methods-techniques-produceimproved-machine-learning.html
KDNuggets. (2017d). Regression Analysis: A Primer. Retrieved March 6, 2017, from http://www.kdnuggets.com/2017/02/regression-analysisprimer.html
KDNuggets. (2017e). Text Analytics, Text Mining. Retrieved March 6, 2017, from http://www.kdnuggets.com/2015/04/statisticscom-textanalytics-text-mining-courses.html
KDNuggets. (2017f). Top Algorithms for Analytics. Retrieved March 7, 2017, from http://www.kdnuggets.com/2011/11/algorithms-foranalytics-data-mining.html
Khousa, E. A., y Atif, Y. (2014). A Learning Analytics Approach to Career Readiness Development in Higher Education. In International Conference on Web-Based Learning (pp. 133-141). Springer.
Khousa, E. A., Atif, Y., y Masud, M. M. (2015). A social learning analytics approach to cognitive apprenticeship. Smart Learning Environments, 2(1), 14.
Kim, J., Jo, I.-H., y Park, Y. (2016). Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, 17(1), 13-24.
Kitchenham, B. A., y Charters, S. (2007). Procedures for Performing Systematic Literature Reviews in Software Engineering. Keele University y Durham University, UK.
Kovanović, V., GaÅ¡ević, D., Dawson, S., Joksimović, S., Baker, R. S., y Hatala, M. (2015). Penetrating the black box of time-on-task estimation. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 184-193). ACM.
Lockyer, L., y Dawson, S. (2011). Learning designs and learning analytics. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 153-156). ACM.
Lotsari, E., Verykios, V. S., Panagiotakopoulos, C., y Kalles, D. (2014). A learning analytics methodology for student profiling. In Hellenic Conference on Artificial Intelligence (pp. 300-312). Springer.
Manso-Vázquez, M., y Llamas-Nistal, M. (2015). A Monitoring System to Ease Self-Regulated Learning Processes. IEEE Revista Iberoamericana de Tecnologias Del Aprendizaje, 10(2), 52-59.
Mayor, E. (2015). Learning Predictive Analytics with R. McKay, T., Miller, K., y Tritz, J. (2012). What to do with actionable intelligence: E 2 Coach as an intervention engine. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 88-91). ACM.
Menchaca, I., Guenaga, M., y Solabarrieta, J. (2015). Project-Based Learning: Methodology and Assessment Learning Technologies and Assessment Criteria. In Design for Teaching and Learning in a Networked World (pp. 601-604). Springer.
Nespereira, C. G., Elhariri, E., El-Bendary, N., Vilas, A. F., y Redondo, R. P. D. (2016). Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning. In The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt (pp. 47-56). Springer.
Øhrstrøm, P., Sandborg-Petersen, U., Thorvaldsen, S., y Ploug, T. (2013). Teaching logic through web-based and gamified quizzing of formal arguments. In European Conference on Technology Enhanced Learning (pp. 410-423). Springer.
Pardo, A., Mirriahi, N., Dawson, S., Zhao, Y., Zhao, A., y GaÅ¡ević, D. (2015). Identifying learning strategies associated with active use of video annotation software. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 255-259). ACM.
Peña, A. (2017). Learning Analytics : Fundaments , Applications , and A View of the Current State of the Art to (94th ed.). Mexico DF: Springer Berlin Heidelberg.
Piety, P. J., Hickey, D. T., y Bishop, M. J. (2014). Educational data sciences: framing emergent practices for analytics of learning, organizations, and systems. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 193-202). ACM.
Pravilovic, S., Bilancia, M., Appice, A., y Malerba, D. (2017). Using multiple time series analysis for geosensor data forecasting. Information Sciences, 380, 31-52. https://doi.org/http://dx.doi.org/10.1016/j.ins.2016.11.001
Prinsloo, P., Slade, S., y Galpin, F. (2012). Learning analytics: challenges, paradoxes and opportunities for mega open distance learning institutions. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 130-133). ACM.
Ray, S. (2015). Understanding Support Vector Machine algorithm from examples. Retrieved March 6, 2017, from https://www.analyticsvidhya.com/blog/2015/10/understaing-supportvector-machine-example-code/
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. https://doi.org/10.1109/TSMCC.2010.2053532
Scheffel, M., Niemann, K., Leony, D., Pardo, A., Schmitz, H.-C., Wolpers, M., y Kloos, C. D. (2012). Key action extraction for learning analytics. In European Conference on Technology Enhanced Learning (pp. 320-333). Springer.
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https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/download/489/660
https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/download/489/1097
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Text
Publication
institution TECNOLOGICO DE ANTIOQUIA INSTITUCION UNIVERSITARIA
thumbnail https://nuevo.metarevistas.org/TECNOLOGICODEANTIOQUIAINSTITUCIONUNIVERSITARIA/logo.png
country_str Colombia
collection Cuaderno activa
title Análisis del aprendizaje: una revisión sistemática de literatura.
spellingShingle Análisis del aprendizaje: una revisión sistemática de literatura.
Gil Vera, Victor Daniel
learning analytics
learning to learn
lifelong learning
multimodal learning analytics
data exchange.
análisis del aprendizaje
análisis del aprendizaje multimodal
intercambio de datos
aprender a aprender
aprendizaje permanente.
title_short Análisis del aprendizaje: una revisión sistemática de literatura.
title_full Análisis del aprendizaje: una revisión sistemática de literatura.
title_fullStr Análisis del aprendizaje: una revisión sistemática de literatura.
title_full_unstemmed Análisis del aprendizaje: una revisión sistemática de literatura.
title_sort análisis del aprendizaje: una revisión sistemática de literatura.
title_eng Learning analytics: a systematic literature review.
description La mayoría de algoritmos utilizados en el análisis de datos están diseñados de acuerdo con las capacidades de potencia y flexibilidad más que por su sencillez, y son demasiado complejos de utilizar en el contexto educativo. El objetivo de este trabajo es presentar una revisión de literatura sobre el análisis del aprendizaje en la educación superior: problemas, limitaciones, técnicas y herramientas empleadas. Se utilizó la metodología de la revisión sistemática de literatura para responder a tres preguntas de investigación tomando como base publicaciones científicas. Se concluye que se deben implementar, adaptar o desarrollar algoritmos predeterminados para el contexto educativo y, también, construir herramientas para el análisis de datos educacionales que cuenten con interfaces intuitivas y fáciles de utilizar.
description_eng The majority of algorithms used in the data analysis are designed according to the capacities of power and flexibility rather than for its simplicity and are too complex to use in the educational context. The objective of this paperis to present a literature review on the learning analytics in higher education: problems, limitations, techniques and tools used. The systematicliterature review methodology was used to answer three research questions on the basis of scientific publications. This paper concludes that it mustimplement, adapt or develop algorithms for the educational context and must be build tools for the analysis of educational data with intuitive interfaces and easy to use.
author Gil Vera, Victor Daniel
author_facet Gil Vera, Victor Daniel
topic learning analytics
learning to learn
lifelong learning
multimodal learning analytics
data exchange.
análisis del aprendizaje
análisis del aprendizaje multimodal
intercambio de datos
aprender a aprender
aprendizaje permanente.
topic_facet learning analytics
learning to learn
lifelong learning
multimodal learning analytics
data exchange.
análisis del aprendizaje
análisis del aprendizaje multimodal
intercambio de datos
aprender a aprender
aprendizaje permanente.
topicspa_str_mv análisis del aprendizaje
análisis del aprendizaje multimodal
intercambio de datos
aprender a aprender
aprendizaje permanente.
citationvolume 10
citationissue 1
publisher Tecnológico de Antioquia - Institución Universitaria
ispartofjournal Cuaderno activa
source https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/view/489
language spa
format Article
rights https://creativecommons.org/licenses/by-nc-sa/4.0
Victor Daniel Gil Vera - 2018
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
references Aguiar, E., Chawla, N. V, Brockman, J., Ambrose, G. A., y Goodrich, V. (2014). Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 103-112). ACM.
Asif, R., Merceron, A., y Pathan, M. K. (2015). Investigating performance of students: a longitudinal study. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 108-112). ACM.
Cambruzzi, W. L., Rigo, S. J., y Barbosa, J. L. V. (2015). Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach. J. UCS, 21(1), 23-47.
Clow, D. (2014). Data wranglers: human interpreters to help close the feedback loop. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 49-53). ACM.
Corrigan, O., Smeaton, A. F., Glynn, M., y Smyth, S. (2015). Using Educational Analytics to Improve Test Performance. In Design for Teaching and Learning in a Networked World (pp. 42-55). Springer.
Diebold, F. (2001). Elements of forecasting. (South y W. C. Publishing., Eds.) (2nd ed.). Australia.
Drachsler, H., y Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 120- 129). ACM.
Friedman, V. (2008). Data visualization and infographics. Graphics, Monday Inspiration, 14, 2008.
Gasevic, D., Kovanovic, V., Joksimovic, S., y Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. The International Review of Research in Open and Distributed Learning, 15(5).
Gibson, A., Kitto, K., y Willis, J. (2014). A cognitive processing framework for learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 212-216). ACM.
Grann, J., y Bushway, D. (2014). Competency map: Visualizing student learning to promote student success. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 168-172). ACM.
Grau-Valldosera, J., y Minguillón, J. (2011). Redefining dropping out in online higher education: a case study from the UOC. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 75-80). ACM.
Harrison, S., Villano, R., Lynch, G., y Chen, G. (2015). Likelihood analysis of student enrollment outcomes using learning environment variables: A case study approach. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 141-145). ACM.
Hecking, T., Ziebarth, S., y Hoppe, H. U. (2014). Analysis of dynamic resource access patterns in a blended learning course. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 173-182). ACM.
Holman, C., Aguilar, S., y Fishman, B. (2013). GradeCraft: What can we learn from a gameinspired learning management system? In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 260-264). ACM.
KDnuggets. (2017). Data Science Statistics 101. Retrieved June 3, 2017, from http://www.kdnuggets.com/2016/07/data-sciencestatistics-101.html
KDNuggets. (2017a). Association Rules and the Apriori Algorithm: A Tutorial. Retrieved March 7, 2016, from http://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html
KDNuggets. (2017b). Comparing Clustering Techniques: A Concise Technical Overview. Retrieved June 3, 2017, from http://www.kdnuggets.com/2016/09/comparing-clusteringtechniques-concise-technical-overview.html
KDNuggets. (2017c). Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results. Retrieved March 6, 2017, from http://www.kdnuggets.com/2016/02/ensemble-methods-techniques-produceimproved-machine-learning.html
KDNuggets. (2017d). Regression Analysis: A Primer. Retrieved March 6, 2017, from http://www.kdnuggets.com/2017/02/regression-analysisprimer.html
KDNuggets. (2017e). Text Analytics, Text Mining. Retrieved March 6, 2017, from http://www.kdnuggets.com/2015/04/statisticscom-textanalytics-text-mining-courses.html
KDNuggets. (2017f). Top Algorithms for Analytics. Retrieved March 7, 2017, from http://www.kdnuggets.com/2011/11/algorithms-foranalytics-data-mining.html
Khousa, E. A., y Atif, Y. (2014). A Learning Analytics Approach to Career Readiness Development in Higher Education. In International Conference on Web-Based Learning (pp. 133-141). Springer.
Khousa, E. A., Atif, Y., y Masud, M. M. (2015). A social learning analytics approach to cognitive apprenticeship. Smart Learning Environments, 2(1), 14.
Kim, J., Jo, I.-H., y Park, Y. (2016). Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, 17(1), 13-24.
Kitchenham, B. A., y Charters, S. (2007). Procedures for Performing Systematic Literature Reviews in Software Engineering. Keele University y Durham University, UK.
Kovanović, V., GaÅ¡ević, D., Dawson, S., Joksimović, S., Baker, R. S., y Hatala, M. (2015). Penetrating the black box of time-on-task estimation. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 184-193). ACM.
Lockyer, L., y Dawson, S. (2011). Learning designs and learning analytics. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 153-156). ACM.
Lotsari, E., Verykios, V. S., Panagiotakopoulos, C., y Kalles, D. (2014). A learning analytics methodology for student profiling. In Hellenic Conference on Artificial Intelligence (pp. 300-312). Springer.
Manso-Vázquez, M., y Llamas-Nistal, M. (2015). A Monitoring System to Ease Self-Regulated Learning Processes. IEEE Revista Iberoamericana de Tecnologias Del Aprendizaje, 10(2), 52-59.
Mayor, E. (2015). Learning Predictive Analytics with R. McKay, T., Miller, K., y Tritz, J. (2012). What to do with actionable intelligence: E 2 Coach as an intervention engine. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 88-91). ACM.
Menchaca, I., Guenaga, M., y Solabarrieta, J. (2015). Project-Based Learning: Methodology and Assessment Learning Technologies and Assessment Criteria. In Design for Teaching and Learning in a Networked World (pp. 601-604). Springer.
Nespereira, C. G., Elhariri, E., El-Bendary, N., Vilas, A. F., y Redondo, R. P. D. (2016). Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning. In The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt (pp. 47-56). Springer.
Øhrstrøm, P., Sandborg-Petersen, U., Thorvaldsen, S., y Ploug, T. (2013). Teaching logic through web-based and gamified quizzing of formal arguments. In European Conference on Technology Enhanced Learning (pp. 410-423). Springer.
Pardo, A., Mirriahi, N., Dawson, S., Zhao, Y., Zhao, A., y GaÅ¡ević, D. (2015). Identifying learning strategies associated with active use of video annotation software. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 255-259). ACM.
Peña, A. (2017). Learning Analytics : Fundaments , Applications , and A View of the Current State of the Art to (94th ed.). Mexico DF: Springer Berlin Heidelberg.
Piety, P. J., Hickey, D. T., y Bishop, M. J. (2014). Educational data sciences: framing emergent practices for analytics of learning, organizations, and systems. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 193-202). ACM.
Pravilovic, S., Bilancia, M., Appice, A., y Malerba, D. (2017). Using multiple time series analysis for geosensor data forecasting. Information Sciences, 380, 31-52. https://doi.org/http://dx.doi.org/10.1016/j.ins.2016.11.001
Prinsloo, P., Slade, S., y Galpin, F. (2012). Learning analytics: challenges, paradoxes and opportunities for mega open distance learning institutions. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 130-133). ACM.
Ray, S. (2015). Understanding Support Vector Machine algorithm from examples. Retrieved March 6, 2017, from https://www.analyticsvidhya.com/blog/2015/10/understaing-supportvector-machine-example-code/
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. https://doi.org/10.1109/TSMCC.2010.2053532
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