Abstract
The data analysis has become a fundamental area for knowledge discovery from data extracted from different sources. In that sense, to develop mechanisms, strategies, methodologies that facilitate their use in different contexts, it has become an important need. In this paper, we propose an “Autonomic Cycle Of Data Analysis Tasks” for learning analytic (ACODAT) in the context of online learning environments, which defines a set of tasks of data analysis, whose objective is to improve the learning processes. Each data analysis task interacts with each other, and has different roles: observe the process, analyze and interpret what happens in it, or make decisions in order to improve the learning process. In this paper, we study the application of the autonomic cycle into the contexts of a smart classroom and a virtual learning platform.
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Aguilar, J., Mosquera, D.: Middleware reflexivo para la gestión de aprendizajes conectivistas en ecologias del conocimiento (eco-conectivismo). Lat.-Am. J. Comput. 2(2), 25–31 (2015)
Aguilar, J., Valdivieso, P., Cordero, J., Riofrio, G., Encalada, E.: A general framework for learning analytic in a smart classroom. In: Valencia-García, R., et al. (eds.) CITI 2016. CCIS, vol. 658, pp. 214–225. Springer, Heidelberg (2016)
Aguilar, J., Moreno, K., Hernandez, D., Altamiranda, J., Viloria, M.: Propuesta de un Modelo Educativo Utilizando el Paradigma de la Nube. ReVeCom 1, 1–11 (2014)
Aguilar, J., Fuentes, J., Moreno, K., Dos Santos, O., Portilla, O., Altamiranda, J., Hernandez, D.: Computational platform for the educational model based on the cloud paradigm. In: Proceeding of the 45th Annual Frontiers in Education, pp. 2399–2407 (2015)
Anupamar, S., Vijayalakshmi, M.: Efficiency of data mining techniques in edifying sector. Int. J. Adv. Found. Res. Comput. 1(8), 56–62 (2014)
Ari, J., White, B.: Learning Analytics From Research to Practice. Springer, New York (2014)
Buckingham, S., Ferguson, R.: Social learning analytics. Educ. Technol. Soc. 15(3), 3–26 (2012)
Cruz-Benito, J., Theron, R., Garcia-Penalvo, F., Pizarro, E.: Discovering usage behaviors and engagement in an educational virtual world. Comput. Hum. Behav. 47, 18–25 (2015)
Ferguson, R.: Learning analytics: drivers, developments and challenges. Int. J. Technol. Enhanc. Learn. 4(5/6), 304–317 (2012)
Gomez-Aguilar, D., Hernandez-Garcia, A., Garcia-Penalvo, F., Garcia-Penalvo, J., Theron, R.: Tap into visual analysis of customization of grouping of activities in eLearning. Comput. Hum. Behav. 47, 60–67 (2015)
Hernandez-Garcia, A., Gonzalez-Gonzalez, I., Jimenez-Zarco, A., Chaparro-Pelaez, J.: Applying social learning analytics to message boards in online distance learning: a case study. Comput. Hum. Behav. 47, 68–80 (2015)
Iglesias-Pradas, S., Ruiz-de-Azcarate, C., Agudo-Peregrina, A.: Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies. Comput. Hum. Behav. 47, 81–89 (2015)
Munoz-Merino, P., Ruiperez-Valiente, J., Alario-Hoyos, C., Perez-Sanagustin, M., Delgado, C.: Precise effectiveness strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs. Comput. Hum. Behav. 47, 108–118 (2015)
Ranbaduge, T.: Use of data mining methodologies in evaluating educational data. Int. J. Sci. Res. Publ. 3(11), 25–39 (2013)
Riofrio, G., Encalada, E., Guamn, D., Aguilar, J.: Aplicacin de un general learning analytics framework para analizar la desercin estudiantil en carreras a distancia. In: Proceeding XLI Conferencia Latinoamericana en Informtica, pp. 567–576 (2015)
Sanchez, M., Aguilar, J., Cordero, J., Valdiviezo, P.: A smart learning environment based on cloud learning. Int. J. Adv. Inf. Sci. Technol. 39(39), 39–52 (2015)
Xing, W., Guo, R., Petakovic, E., Goggins, S.: Participation-based student grade prediction model through interpretable Genetic Programming: integrating learning analytics, educational data mining and theory. Comput. Hum. Behav. 47, 168–181 (2015)
Valdiviezo, P., Cordero, J., Aguilar, J., Snchez, M.: Conceptual design of a smart classroom based on multiagent systems. In: International Conference Artificial Intelligence (ICAI 2015), pp. 471–477 (2015)
Acknowledgement
Dr. Aguilar has been partially supported by the Prometeo Project of the Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador.
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Aguilar, J., Buendia, O., Moreno, K., Mosquera, D. (2016). Autonomous Cycle of Data Analysis Tasks for Learning Processes. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., del Cioppo, J., Vera-Lucio, N. (eds) Technologies and Innovation. CITI 2016. Communications in Computer and Information Science, vol 658. Springer, Cham. https://doi.org/10.1007/978-3-319-48024-4_15
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DOI: https://doi.org/10.1007/978-3-319-48024-4_15
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