Análisis del aprendizaje: una revisión sistemática de literatura.

  • Victor Daniel Gil Vera
Palabras clave: análisis del aprendizaje, análisis del aprendizaje multimodal, intercambio de datos, aprender a aprender, aprendizaje permanente.

Resumen

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.

Citas

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., Gašević, 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 Gašević, 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.
Cómo citar
Gil Vera, V. (1). Análisis del aprendizaje: una revisión sistemática de literatura. CUADERNO ACTIVA, 10(10), 15-26. Recuperado a partir de http://ojs.tdea.edu.co/index.php/cuadernoactiva/article/view/489
Número
Sección
Artículos de revisión