Inferring the differential student model in a probabilistic domain using abduction Inference in Bayesian Networks

Educational Data Mining 2010 - 3rd International Conference on Educational Data Mining

Volumn , Issue , 2010, Pages 299-300

  Khodeir, Nabila ^a   Wanas, Nayer ^b   Darwish, Nevin ^c   Hegazy, Nadia ^a  

^a Electronics Research Institute   (Egypt)

^b MICROSOFT   (United States)

^c CAIRO UNIVERSITY   (Egypt)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL RESOURCES; INTELLIGENT TUTORING SYSTEM; REFINED MODEL; STUDENT KNOWLEDGE MODEL; STUDENT MODELS;

ALGORITHMS; BAYESIAN NETWORKS; COMPUTER AIDED INSTRUCTION; DATA MINING; EDUCATION COMPUTING; REFINING;

STUDENTS;

EID: 82455222446     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Brusilovsky, P., Millan, E. User Models for Adaptive Hypermedia and Adaptive Educational Systems, In Brusilovsky, P., Kobsa, A., Nejdl, W.(eds.) The Adaptive Web, LNCS 4321, 2007, pp. 3-53. (Pubitemid 351189193)
2. Burton, R, Brown, J A tutoring and student modelling paradigm for gaming environments, SIGCSE-SIGCUE, 1976, pp. 236-246.
3. Elvira Project, http://www.ia.uned.es/~elvira/index-en.html.
4. Nilsson, D. An efficient algorithm for finding the M most probable configurations in probabilistic expert systems, Stat. and Comp. 1998, 8(2), pp. 159-173.
5. Pahl, C., Kennyand, C. Interactive Correction and Recommendation for Computer Language Learning and Training, IEEE Trans. KDE 2009, 21(6), pp.854-866.