Aplicando minería de datos para descubrir rutas de aprendizaje frecuentes en Moodle

  1. Bogarín Vega, Alejandro 2
  2. Romero Morales, Cristóbal 2
  3. Cerezo Menéndez, Rebeca 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Universidad de Córdoba
    info

    Universidad de Córdoba

    Córdoba, España

    ROR https://ror.org/05yc77b46

Revista:
EDMETIC

ISSN: 2254-0059

Año de publicación: 2016

Título del ejemplar: Estado de la cuestión IV: Innovación e investigación para la mejora educativa

Volumen: 5

Número: 1

Páginas: 73-92

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: EDMETIC

Resumen

In this paper, we apply techniques data mining to discover common learning routes. We have used data from 84 undergraduate college students who followed an online course using Moodle 2.0. We propose to group students firstly starting from data about Moodle’s usage summary and/or the students’ final marks in the course. Then, we use data from Moodle’s logs about each cluster/group of students separately in order to be able to obtain more specific and accurate process models of students’ behaviour.

Referencias bibliográficas

  • AZEVEDO, R., BEHNAGH, R., DUFFY, M., HARLEY, J., y TREVORS, G. (2012). Metacognition and self-regulated learning in student-centered leaning environments. Theoretical foundations of student-centered learning environments, 171-197.
  • KLÖSGEN, W., y ZYTKOW, J. M. (2002). Handbook of data mining and knowledge discovery. Oxford: University Press, Inc.
  • MULDNER, K., BURLESON, W., VAN DE SANDE, B., y VANLEHN, K. (2011). An analysis of students’ gaming behaviors in an intelligent tutoring system: predictors and impacts. User Modeling and User-Adapted Interaction, 21(1-2), 99-135.
  • PECHENIZKIY, M., TRCKA, N., VASILYEVA, E., VAN DER AALST, W., y DE BRA, P. (2009). Process Mining Online Assessment Data. International Working Group on Educational Data Mining.
  • PEDRAZA-PEREZ, R., ROMERO, C., &y VENTURA, S. (2011). A Java desktop tool for mining Moodle data. En Proceedings of 4th International Conference on Educational Data Mining (pp. 319-320).
  • PERERA, D., KAY, J., KOPRINSKA, I., YACEF, K., y ZAÏANE, O. R. (2009). Clustering and sequential pattern mining of online collaborative learning data. Knowledge and Data Engineering, IEEE Transactions on, 21(6), 759-772.
  • RABBANY, R., TAKAFFOLI, M., y ZAÏANE, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. En Proceedings of educational data mining.
  • ROMERO, C., y VENTURA, S. (2010). Educational data mining: a review of the state of the art. Systems, Man, and Cybernetics, Part C: Applications and Reviews. IEEE Transactions on, 40(6), 601-618.
  • ROMERO, C., VENTURA, S., y GARCÍA, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.
  • ROMERO, C., VENTURA, S., ZAFRA, A., y BRA, P. D. (2009). Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Computers & Education, 53(3), 828-840.
  • SIEMENS, G., & D BAKER, R. S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. En Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
  • SOUTHAVILAY, V., YACEF, K., y CALVO, R. A. (2010, June). Process Mining to Support Students' Collaborative Writing. En EDM (pp. 257-266).
  • TRCKA, N., & PECHENIZKIY, M. (2009, November). From local patterns to global models: Towards domain driven educational process mining. En Intelligent Systems Design and Applications, 2009. ISDA'09. Ninth International Conference on (pp. 1114-1119). IEEE.
  • VAN DER AALST, W. M. (2011). Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg.
  • WITTEN, I. H., y FRANK, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Fuente de los datos: Dialnet