Análisis del aprendizaje: una revisión sistemática de literatura.
Learning analytics: a systematic literature review.
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Gil Vera, V. D. (2021). Análisis del aprendizaje: una revisión sistemática de literatura. Cuaderno Activa, 10(1), 15–26. https://doi.org/10.53995/20278101.489
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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.
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Referencias (VER)
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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.
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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),
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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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Sinclair, J., y Kalvala, S. (2015). Engagement measures in massive open online courses. In International Workshop on Learning Technology for Education in Cloud (pp. 3-15). Springer.
Swenson, J. (2014). Establishing an ethical literacy for learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 246-250). ACM.
Tervakari, A.-M., Marttila, J., Kailanto, M., Huhtamäki, J., Koro, J., y Silius, K. (2013). Developing learning analytics for TUT circle. In Open and Social Technologies for Networked Learning (pp. 101-110). Springer.
Vahdat, M., Oneto, L., Anguita, D., Funk, M., y Rauterberg, M. (2015). A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator. In Design for Teaching and Learning in a Networked World (pp. 352-366). Springer.
Vozniuk, A., Holzer, A., y Gillet, D. (2014). Peer assessment based on ratings in a social media course. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 133-137). ACM.
Westera, W., Nadolski, R., y Hummel, H. (2013). Learning analytics in serious gaming: uncovering the hidden treasury of game log files. In International Conference on Games and Learning Alliance (pp. 41-52). Springer.
Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 203-211). 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.
Scikit-Learn. (2017). Decision Trees. Retrieved March 6, 2017, from http://scikit-learn.org/stable/modules/tree.html#multi-output-problems
Simsek, D., Sándor, Á., Shum, S. B., Ferguson, R., De Liddo, A., y Whitelock, D. (2015). Correlations between automated rhetorical analysis and tutors’ grades on student essays. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 355-359). ACM.
Sinclair, J., y Kalvala, S. (2015). Engagement measures in massive open online courses. In International Workshop on Learning Technology for Education in Cloud (pp. 3-15). Springer.
Swenson, J. (2014). Establishing an ethical literacy for learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 246-250). ACM.
Tervakari, A.-M., Marttila, J., Kailanto, M., Huhtamäki, J., Koro, J., y Silius, K. (2013). Developing learning analytics for TUT circle. In Open and Social Technologies for Networked Learning (pp. 101-110). Springer.
Vahdat, M., Oneto, L., Anguita, D., Funk, M., y Rauterberg, M. (2015). A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator. In Design for Teaching and Learning in a Networked World (pp. 352-366). Springer.
Vozniuk, A., Holzer, A., y Gillet, D. (2014). Peer assessment based on ratings in a social media course. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 133-137). ACM.
Westera, W., Nadolski, R., y Hummel, H. (2013). Learning analytics in serious gaming: uncovering the hidden treasury of game log files. In International Conference on Games and Learning Alliance (pp. 41-52). Springer.
Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (pp. 203-211). ACM.
