Process mining from educational data

Handbook of Educational Data Mining

Volumn , Issue , 2010, Pages 123-142

  Trčka, Nikola ^a   Pechenizkiy, Mykola ^a   van der Aalst, Wil ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85008351995     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b10274     Document Type: Chapter

References (13)

1. Dekker, G., Pechenizkiy M., and Vleeshouwers, J., Predicting students drop out: A case study, in Proceedings of the 2nd International Conference on Educational Data Mining (EDM’09), Cordoba, Spain, pp. 41-50, July 1-3, 2009.
2. Zafra, A. and Ventura, S., Predicting student grades in learning management systems with multiple instance genetic programming, in Proceedings of the 2nd International Conference on Educational Data Mining (EDM’09), Cordoba, Spain, pp. 309-318, July 1-3, 2009.
3. Perera, D., Kay, J., Koprinska, I., Yacef, K., and Zaïane, O.R., Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759-772, 2009.
4. Pechenizkiy, M., Trcka, N., Vasilyeva, E., van der Aalst, W., and De Bra, P., Process mining the student assessment data, in Proceedings of the 2nd International Conference on Educational Data Mining (EDM’09), Cordoba, Spain, pp. 279-288, July 1-3, 2009.
5. Romero, C., Ventura, S., and García, E., Data mining in course management systems: MOODLE case study and tutorial. Comput. Educ. 51, 368-384, 2008.
6. Romero, C. and S. Ventura, S., Educational data mining: A survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135-146, 2007.
7. van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., and van der Aalst, W.M.P., The ProM framework: A new era in process mining tool support, in Proceedings of the ICATPN 2005, Ciardo, G., Darondeau, P., Eds., LNCS 3536, Springer, Heidelberg, Germany, 2005, pp. 444-454.
8. Rozinat, A. and van der Aalst, W.M.P., Conformance checking of processes based on monitoring real behavior. Inform. Syst. 33(1), 64-95, 2008.
9. van der Aalst, W.M.P., Weijters, A.J.M.M., and Maruster, L., Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128-1142, 2004.
10. Günther, C.W. and van der Aalst, W.M.P., Fuzzy mining: Adaptive process simplification based on multi-perspective metrics, in Proceedings of the International Conference on Business Process Management (BPM 2007), Alonso, G., Dadam, P., and Rosemann, M., Eds., LNCS 4714, Springer-Verlag, Berlin, Germany, 2007, pp. 328-343.
11. Song, M. and van der Aalst, W.M.P., Supporting process mining by showing events at a glance, in Proceedings of the 7th Annual Workshop on Information Technologies and Systems (WITS’07), Chari, K. and Kumar, A., Eds., Montreal, Canada, 2007, pp. 139-145.
12. Günther, C.W. and van der Aalst, W.M.P., A generic import framework for process event logs, in Proceedings of the Business Process Management Workshop, 2006, pp. 81-92 (see also http://promimport.sourceforge.net/).
13. Trcka, N. and Pechenizkiy, M., From local patterns to global models: Towards domain driven educational process mining, in Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA’09), Pisa, Italy, IEEE CS Press, November 30-December 2, 2009.