Abstract
Learning Analytic is a useful tool in the context of the learning process, in order to improve the educational environment. In previous works, we have proposed autonomic cycles of learning Analytic tasks, in order to improve the learning process in smart classrooms. One aspect to be considered by the autonomic cycles is their adaptability to the formation of competences, assuming that a student has competences that must be strengthened during the learning process. In this paper, we propose the utilization of competences to guide the adaptation process of a learning environment. Particularly, we propose the extensions of the autonomic cycles for smart classrooms, using the idea of competences. In this case, we define the competences as a service, to help the autonomic cycles in their processes of adaptation.
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References
De Leenheer, P., Christiaens, S., Meersman, R.: Business semantics management: a case study for competency-centric HRM. Comput. Ind. 61(8), 760–775 (2010)
Dorn, J., Pichlmair, M.: A competence management system for universities. In: Proceedings of the European Conference on Information Systems (2009)
Malzahn, N., Ziebarth, S., Hoppe, H.: Semi-automatic creation and exploitation of competence ontologies for trend aware profiling, matching and planning. Knowl. Manage. E-Learn. 5(1), 84–103 (2013)
Valdiviezo-Díaz, P., Aguilar, J., Cordero, J., Sánchez, M.: Conceptual design of a smart classroom based on multiagent systems. In: ICAI 2015 International Conference on Artificial Intelligence, pp. 471–477 (2015b)
Siemens, G.: Learning analytics: envisioning a research discipline and a domain of practice. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 4–8 (2012)
Aguilar, J., Valdiviezo-Díaz, P.: Learning analytic in a smart classroom to improve the eEducation. In: Proceedings of Fourth International Conference on eDemocracy and eGovernment, pp. 43–50 (2017)
Aguilar, J., Valdiviezo, P., Cordero, J., Riofrio, G., Encalada, E.: A general framework for learning analytic in a smart classroom. In: Valencia-García, R., et al. (eds.) Technologies and innovation. Communications Computer and Information Science Series, vol. 658, pp. 214–225. Springer, Cham (2016)
Brooks, C., Greer, J., Gutwin, C.: The data-assisted approach to building intelligent technology-enhanced learning environments. In: Larusson, J., White, B. (eds.) Learning Analytics, pp. 123–156. Springer, New York (2014)
Cruz-Benito, J., Therón, R., García-Peñalvo, F.J., Lucas, E.P.: Discovering usage behaviors and engagement in an Educational Virtual World. Comput. Hum. Behav. 47, 18–25 (2015)
García-Saiz, D., Zorrilla, M.: E-learning web miner: a data mining application to help instructors involved in virtual courses. In: Educational Data Mining 2011 (2010)
Gómez-Aguilar, D.A., Hernández-García, Á., García-Peñalvo, F.J., Therón, R.: Tap into visual analysis of customization of grouping of activities in eLearning. Comput. Hum. Behav. 47, 60–67 (2015)
Hershkovitz, A., Nachmias, R.: Learning about online learning processes and students’ motivation through web usage mining. Interdisc. J. E-Learn. Learn. Objects 5(1), 197–214 (2009)
Kalles, D., Pierrakeas, C.: Analyzing student performance in distance learning with genetic algorithms and decision trees. Appl. Artif. Intell. 20(8), 655–674 (2006)
Krumm, A.E., Waddington, R.J., Teasley, S.D., Lonn, S.: A learning management system-based early warning system for academic advising in undergraduate engineering. In: Larusson, J., White, B. (eds.) Learning Analytics, pp. 103–119. Springer, New York (2014)
Muñoz-Merino, P.J., Ruipérez-Valiente, J.A., Alario-Hoyos, C., Pérez-Sanagustín, M., Kloos, C.D.: Precise effectiveness strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs. Comput. Hum. Behav. 47, 108–118 (2015)
Papamitsiou, Z.K., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educ. Technol. Soc. 17(4), 49–64 (2014)
Pardo, A.: Designing learning analytics experiences. In: Larusson, J., White, B. (eds.) Learning analytics, pp. 15–38. Springer, New York (2014)
Gonzalez, A., Aguilar, J.: Semantic architecture for the analysis of the academic and occupational profiles based on competences. Contemp. Eng. Sci. 8(33), 1551–1563 (2015)
Verbert, K., Govaerts, S., Duval, E., Santos, J.L., Van Assche, F., Parra, G., Klerkx, J.: Learning dashboards: an overview and future research opportunities. Pers. Ubiquitous Comput. 18(6), 1499–1514 (2014)
Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: Using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM (2012)
Dollár, A., Steif, P.S.: Web-based statics course with learning dashboard for instructors. In: Proceedings of Computers and Advanced Technology in Education (CATE) (2012)
Govaerts, S., Verbert, K., Duval, E., Pardo, A.: The student activity meter for awareness and self-reflection. In: CHI 2012 Extended Abstracts on Human Factors in Computing Systems, pp. 869–884. ACM (2012)
Teixeira, A., Mota, J., García-Cabot, A., García-Lopéz, E., de-Marcos, L.: A new competence-based approach for personalizing MOOCs in a mobile collaborative and networked environment. RIED Revista Iberoamericana de Educación a Distancia, 19(1), 143–160 (2016)
Gluga, R., Kay, J., Lever, T.: Foundations for modeling university curricula in terms of multiple learning goal sets. IEEE Trans. Learn. Technol. 6(1), 25–37 (2013)
Nussbaumer, A., Hillemann, E.C., Gütl, C., Albert, D.: A competence-based service for supporting self-regulated learning in virtual environments. J. Learn. Anal. 2(1), 101–133 (2015)
Sánchez, M., Aguilar, J., Cordero, J., Valdiviezo-Díaz, P., Barba-Guamán, L., Chamba-Eras, L.: Cloud computing in smart educational environments: application in learning analytics as service. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Teixeira, M.M. (eds.) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol. 444. Springer, Cham (2016). doi:10.1007/978-3-319-31232-3_94
Sánchez, 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 (2015a)
Sánchez, M., Aguilar, J., Cordero, J., Valdiviezo, P.: Basic features of a reflective middleware for intelligent learning environment in the cloud (IECL). In: 2015 Asia-Pacific Conference on Computer Aided System Engineering, pp. 1–6 (2015b)
Aguilar, J., Sánchez, M., Cordero, J., Valdiviezo-Díaz, P., Barba-Guamán, L., Chamba-Eras, L.: Learning analytics tasks as services in smart classrooms. Universal Access in the Information Society (2017)
Valdiviezo-Díaz, P., Cordero, J., Reátegui, R., Aguilar, J.: A business intelligence model for online tutoring process. In: Proceedings - Frontiers in Education Conference, FIE, vol. 2015 (2015a). http://doi.org/10.1109/FIE.2015.7344385
Riofrío, G., Encalada, E., Aguilar, J.: Learning analytics focused on student behavior. case study: dropout in distance learning institutions. CLEI Electron. J. 20(1) (2017)
Paquette, G.: A competency-based ontology for learning design repositories. Int. J. Adv. Comput. Sci. Appl. 5(1), 55–62 (2014)
De Laat, M., Prinsen, F.R.: Social learning analytics: navigating the changing settings of higher education. Res. Pract. Assess. 9, 51–60 (2014)
Ferguson, R., Shum, S.B.: Social learning analytics: five approaches. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 23–33 (2012)
Shum, S.B., Ferguson, R.: Social learning analytics. Educ. Technol. Soc. 15(3), 3–26 (2012)
Guevara, C., Aguilar, J., González-Eras, A.: The model of adaptive learning objects for virtual environments instanced by the competences. Adv. Sci. Technol. Eng. Syst. J. 2(3), 345–355 (2017)
Aguilar, J., Cordero, J., Buendía, O.: Specification of the autonomic cycles of learning analytic tasks for a smart classroom. J. Educ. Comput. Res. (2017). Accepted for publication
Acknowledgment
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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González-Eras, A., Buendia, O., Aguilar, J., Cordero, J., Rodriguez, T. (2017). Competences as Services in the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., Del Cioppo, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2017. Communications in Computer and Information Science, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-67283-0_16
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DOI: https://doi.org/10.1007/978-3-319-67283-0_16
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