Evaluating Student Learning Using Concept Maps And Markov Chains
Keywords
Artificial intelligence; Concept maps; Finite; Markov chains; Student evaluation; Transition matrix; XML parsing
Abstract
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.
Publication Date
5-1-2015
Publication Title
Expert Systems with Applications
Volume
42
Issue
7
Number of Pages
3306-3314
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/j.eswa.2014.12.016
Copyright Status
Unknown
Socpus ID
84920973912 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84920973912
STARS Citation
Gurupur, Varadraj P.; Pankaj Jain, G.; and Rudraraju, Ramaraju, "Evaluating Student Learning Using Concept Maps And Markov Chains" (2015). Scopus Export 2015-2019. 728.
https://stars.library.ucf.edu/scopus2015/728