Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic

    Authors

    G. P. Jain; V. P. Gurupur; J. L. Schroeder;E. D. Faulkenberry

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    IEEE Trans. Learn. Technol.

    Keywords

    Concept maps; evaluation; probability distributions; XML parsers; PROBABILITY DENSITY-FUNCTION; MARKOV-CHAINS; KNOWLEDGE; Computer Science, Interdisciplinary Applications; Education &; Educational Research

    Abstract

    In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of the concepts identified in the concept map developed by the student. The evaluation of a student's understanding of the topic is assessed by analyzing the curve of the graph generated by this tool. This technique makes extensive use of XML parsing to perform the required evaluation. The tool was successfully tested with students from two undergraduate courses and the results of testing are described in this paper.

    Journal Title

    Ieee Transactions on Learning Technologies

    Volume

    7

    Issue/Number

    3

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    267

    Last Page

    279

    WOS Identifier

    WOS:000346319400008

    ISSN

    1939-1382

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