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Volumn , Issue , 2012, Pages

The impact on individualizing student models on necessary practice opportunities

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED INSTRUCTION; DATA MINING; SCATTERING PARAMETERS;

EID: 85084011830     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (95)

References (13)
  • 1
    • 84884445705 scopus 로고    scopus 로고
    • More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian estimation
    • S. J. d. Baker, A. T. Corbett, and V. Aleven. More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian estimation. In ITS, 2008.
    • (2008) ITS
    • d. Baker, S.J.1    Corbett, A.T.2    Aleven, V.3
  • 2
    • 70349848238 scopus 로고    scopus 로고
    • Difficulties in inferring student knowledge from observations (and why you should care)
    • J. Beck. Difficulties in inferring student knowledge from observations (and why you should care). In AIED Workshop on EDM, 2007.
    • (2007) AIED Workshop on EDM
    • Beck, J.1
  • 3
    • 70349856491 scopus 로고    scopus 로고
    • Identifiability: A fundamental problem of student modeling
    • J. Beck and K. Chang. Identifiability: A fundamental problem of student modeling. In UM, 2007.
    • (2007) UM
    • Beck, J.1    Chang, K.2
  • 5
    • 70349882493 scopus 로고    scopus 로고
    • Learning factors analysis - a general method for cognitive model evaluation and improvement
    • H. Cen, K. Koedinger, and B. Junker. Learning factors analysis - a general method for cognitive model evaluation and improvement. In ITS, 2006.
    • (2006) ITS
    • Cen, H.1    Koedinger, K.2    Junker, B.3
  • 6
    • 85072303068 scopus 로고    scopus 로고
    • Is over practice necessary? improving learning efficiency with the cognitiive tutor using educational data mining
    • H. Cen, K. Koedinger, and B. Junker. Is Over Practice Necessary? Improving Learning Efficiency with the Cognitiive Tutor using Educational Data Mining. In AIED, 2007.
    • (2007) AIED
    • Cen, H.1    Koedinger, K.2    Junker, B.3
  • 7
    • 84857471334 scopus 로고    scopus 로고
    • Instructional factors analysis: A cognitive model for multiple instructional interventions
    • M. Chi, K. Koedinger, G. Gordon, P. Jordan, and K. VanLehn. Instructional factors analysis: A cognitive model for multiple instructional interventions. In EDM, 2011.
    • (2011) EDM
    • Chi, M.1    Koedinger, K.2    Gordon, G.3    Jordan, P.4    VanLehn, K.5
  • 8
    • 0000262490 scopus 로고
    • Knowledge tracing: Modeling the acquisition of procedural knowledge
    • A. Corbett and J. Anderson. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4:253–278, 1995.
    • (1995) User Modeling and User-Adapted Interaction , vol.4 , pp. 253-278
    • Corbett, A.1    Anderson, J.2
  • 10
    • 80052417579 scopus 로고    scopus 로고
    • Modeling individualization in a bayesian networks implementation of knowledge tracing
    • Z. Pardos and N. Heffernan. Modeling individualization in a Bayesian networks implementation of knowledge tracing. In UMAP, 2010.
    • (2010) UMAP
    • Pardos, Z.1    Heffernan, N.2
  • 11
    • 84857478510 scopus 로고    scopus 로고
    • Using dirichlet priors to improve model parameter plausibility
    • D. Rai, Y. Gong, and J. Beck. Using Dirichlet priors to improve model parameter plausibility. In EDM, 2009.
    • (2009) EDM
    • Rai, D.1    Gong, Y.2    Beck, J.3
  • 12
    • 85072296300 scopus 로고    scopus 로고
    • Using multiple dirichlet distributions to improve model parameter plausibility
    • D. Rai, Y. Gong, and N. Heffernan. Using multiple Dirichlet distributions to improve model parameter plausibility. In EDM, 2010.
    • (2010) EDM
    • Rai, D.1    Gong, Y.2    Heffernan, N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.