Diagnostic, predictive and compositional modeling with data mining in integrated learning environments

被引:11
|
作者
Lee, Chien-Sing [1 ]
机构
[1] Multimedia Univ, Fac Informat Technol, Cyberjaya 63100, Selangor, Malaysia
关键词
intelligent tutoring systems; interactive learning environments; multimedia/hypermedia systems; diagnostic; predictive and compositional modeling; architectural and design patterns;
D O I
10.1016/j.compedu.2005.10.010
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Models represent a set of generic patterns to test hypotheses. This paper presents the CogMoLab student model in the context of an integrated learning environment. Three aspects are discussed: diagnostic and predictive modeling with respect to the issues of credit assignment and scalability and compositional modeling of the student profile in the context of an intelligent tutoring system/adaptive hypermedia learning system architectural pattern. The SOM-PCA, a collaborative-based data mining approach, is shown to be reusable for all three purposes above, enabling fast, objective implementations without requiring much intensive data collection. (C) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:562 / 580
页数:19
相关论文
共 50 条
  • [31] New Attitude to Learning in Virtual Environments - Mining Physiological Data for Automated Feedback
    Lustigova, Zdena
    Dufresne, Aude
    Courtemanche, Francois
    HUMAN-COMPUTER INTERACTION, 2010, 332 : 297 - +
  • [32] Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
    Gordon-Rodriguez, Elliott
    Quinn, Thomas P.
    Cunninghham, John P.
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35, NEURIPS 2022, 2022,
  • [33] A comparative study of machine learning algorithms applied to predictive toxicology data mining
    Neagu, Daniel C.
    Guo, Gongde
    Trundle, Paul R.
    Cronin, Mark T. D.
    ATLA-ALTERNATIVES TO LABORATORY ANIMALS, 2007, 35 (01): : 25 - 32
  • [34] Predictive Machine Learning Approach for Complex Problem Solving Process Data Mining
    Pejic, Aleksandar
    Molcer, Piroska Stanic
    ACTA POLYTECHNICA HUNGARICA, 2021, 18 (01) : 45 - 63
  • [35] Interest mining in virtual learning environments
    Gu, Rong
    Zhu, Miaoliang
    Zhao, Liying
    Zhang, Ningning
    ONLINE INFORMATION REVIEW, 2008, 32 (02) : 133 - 146
  • [36] Secure Learning and Mining in Adversarial Environments
    Li, Bo
    2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), 2015, : 1538 - 1539
  • [37] Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods
    Lee, Jung-Hyun
    Sameen, Maher Ibrahim
    Pradhan, Biswajeet
    Park, Hyuck-Jin
    GEOMORPHOLOGY, 2018, 303 : 284 - 298
  • [38] Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis
    Zhang, Haolong
    Zhang, Haoling
    Yang, Huadi
    Shuid, Ahmad Naqib
    Sandai, Doblin
    Chen, Xingbei
    FRONTIERS IN GENETICS, 2023, 14
  • [40] A data mining architecture for clustered environments
    Ashrafi, MZ
    Taniar, D
    Smith, KA
    APPLIED PARALLEL COMPUTING: ADVANCED SCIENTIFIC COMPUTING, 2002, 2367 : 89 - 98