Evaluating student learning using concept maps and Markov chains

被引：14

作者：

Gurupur, Varadraj P. ^{[1
]}

Jain, G. Pankaj ^{[2
]}

Rudraraju, Ramaraju ^{[3
]}

机构：

[1] Univ Cent Florida, Dept Hlth Management & Informat, Orlando, FL 32816 USA

[2] Texas A&M Univ, Dept Comp Sci & Informat Syst, Commerce, TX 75428 USA

[3] Univ Alabama Birmingham, Dept Elect & Comp Engn, Birmingham, AL 35294 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2015年 / 42卷 / 07期

关键词：

Concept maps; Student evaluation; Artificial intelligence; Finite Markov chains; Transition matrix; XML parsing; SYSTEM;

D O I：

10.1016/j.eswa.2014.12.016

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we describe a tool that can be effectively used to evaluate student learning outcomes using concept maps and Markov chain analysis. The main purpose of this tool is to advance the use of artificial intelligence techniques by using concept maps and Markov chains in evaluating a student's understanding of a particular topic of study using concept maps. The method used in the tool makes use of XML parsing to perform the required evaluation. For the purpose of experimenting this tool we have taken into consideration concept maps developed by students enrolled in two different courses in Computer Science. The result of this experimentation is also discussed. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：3306 / 3314

页数：9

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