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Evaluation module based on Bayesian networks to Intelligent Tutoring Systems

机译:基于贝叶斯网络的智能教学系统评估模块

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Assessing knowledge acquisition by the student is the primary task of an Intelligent Tutoring System (ITS). Assessment is needed to adapt learning materials and activities to student's capacities. In this paper, a proposal to infer the level of knowledge possessed by the student is presented. A general structure of an ITS is shown, an evaluation module based on Bayesian network is proposed. The module mainly based on a test was implemented to know what student knows. During the test, the software system chooses the new questions based on the responses to the previous ones, that is, the software system makes an adaption in real time. A network of concepts was used to get the inferences, which contains the relationships between concepts. Evaluation module could infer many questions and concepts through the relations and the probabilistic inference of the Bayesian network. It information easily can be used to reinforce weak topics in order to cover the student's needs. Given the positive evidence is considered that testing the rest of variable examined in the Bayesian network can provide better accurate in the diagnostic of student' knowledge possession. (C) 2016 Elsevier Ltd. All rights reserved.
机译:评估学生获得的知识是智能辅导系统(ITS)的主要任务。需要评估以使学习材料和活动适应学生的能力。在本文中,提出了一个推断学生拥有的知识水平的建议。给出了ITS的总体结构,提出了一种基于贝叶斯网络的评估模块。该模块主要基于测试,旨在了解学生的知识。在测试过程中,软件系统根据对先前问题的回答选择新问题,即软件系统实时进行适应。使用概念网络来获取推断,其中包含概念之间的关系。评估模块可以通过贝叶斯网络的关系和概率推断来推断许多问题和概念。它可以轻松地用于增强薄弱主题的信息,从而满足学生的需求。假设有积极的证据,则对贝叶斯网络中检查的其余变量进行测试可以在诊断学生的知识占有方面提供更好的准确性。 (C)2016 Elsevier Ltd.保留所有权利。

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