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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/55174


    Title: A simulation-based experience in learning structures of Bayesian networks to represent how students learn composite concepts
    Authors: Liu, Chao-Lin
    Contributors: 政大資訊科學系
    Keywords: Student modelling;learning patterns;bayesian networks;computer-assisted cognitive modelling
    Date: 2008-09
    Issue Date: 2012-11-02 16:47:21 (UTC+8)
    Abstract: Composite concepts result from the integration of multiple basic concepts by students to form high- level knowledge, so information about how students learn composite concepts can be used by instructors to facilitate students` learning, and the ways in which computational techniques can assist the study of the integration process are therefore intriguing for learning, cognition, and computer scientists. We provide an exploration of this problem using heuristic methods, search methods, and machine-learning techniques, while employing Bayesian networks as the language for representing the student models. Given experts` expectation about students and simulated students` responses to test items that were designed for the concepts, we try to find the Bayesian-network structure that best represents how students learn the composite concept of interest. The experiments were conducted with only simulated students. The accuracy achieved by the proposed classification methods spread over a wide range, depending on the quality of collected input evidence. We discuss the experimental procedures, compare the experimental results observed in certain experiments, provide two ways to analyse the influences of Q-matrices on the experimental results, and we hope that this simulation-based experience may contribute to the endeavours in mapping the human learning process.
    Relation: International Journal of Artificial Intelligence in Education, Vol.18, No.3, pp.237-285. (APA PsycINFO)
    Data Type: article
    Appears in Collections:[Department of Computer Science ] Periodical Articles

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