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Volumn 6738 LNAI, Issue , 2011, Pages 377-384

Clustering students to generate an ensemble to improve standard test score predictions

Author keywords

Clustering; Dynamic Assessment; Educational Data Mining; Ensemble Learning; Intelligent Tutoring Systems; Regression

Indexed keywords

CLUSTERING; DYNAMIC ASSESSMENT; EDUCATIONAL DATA MINING; ENSEMBLE LEARNING; INTELLIGENT TUTORING SYSTEM; REGRESSION;

EID: 79959288180     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21869-9_49     Document Type: Conference Paper
Times cited : (47)

References (15)
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    • Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset
    • in press
    • Pardos, Z.A., Heffernan, N.T.: Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. Journal of Machine Learning Research C & WP (in press 2011).
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  • 7
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    • Dynamic Assessment: One Approach and Some Initial Data
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    • Ensemble methods in machine learning
    • Kittler, J., Roli, F. (eds.) First International workshop on Multiple Classifier Systems Springer, New York
    • Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) First International workshop on Multiple Classifier Systems. LNCS, pp. 1-15. Springer, New York (2000).
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    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
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    • Dietterich, T.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.