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Competences as Services in the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom

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Technologies and Innovation (CITI 2017)

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

  1. De Leenheer, P., Christiaens, S., Meersman, R.: Business semantics management: a case study for competency-centric HRM. Comput. Ind. 61(8), 760–775 (2010)

    Article  Google Scholar 

  2. Dorn, J., Pichlmair, M.: A competence management system for universities. In: Proceedings of the European Conference on Information Systems (2009)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Chapter  Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. Kalles, D., Pierrakeas, C.: Analyzing student performance in distance learning with genetic algorithms and decision trees. Appl. Artif. Intell. 20(8), 655–674 (2006)

    Article  Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. 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)

    Google Scholar 

  17. Pardo, A.: Designing learning analytics experiences. In: Larusson, J., White, B. (eds.) Learning analytics, pp. 15–38. Springer, New York (2014)

    Google Scholar 

  18. 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)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. 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)

    Article  Google Scholar 

  26. 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

    Google Scholar 

  27. 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)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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

  31. 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)

    Google Scholar 

  32. Paquette, G.: A competency-based ontology for learning design repositories. Int. J. Adv. Comput. Sci. Appl. 5(1), 55–62 (2014)

    Google Scholar 

  33. De Laat, M., Prinsen, F.R.: Social learning analytics: navigating the changing settings of higher education. Res. Pract. Assess. 9, 51–60 (2014)

    Google Scholar 

  34. 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)

    Google Scholar 

  35. Shum, S.B., Ferguson, R.: Social learning analytics. Educ. Technol. Soc. 15(3), 3–26 (2012)

    Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. 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

    Google Scholar 

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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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Correspondence to Jose Aguilar .

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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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