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

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...

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Bibliographic Details
Published in:IEEE transactions on learning technologies 2014-07, Vol.7 (3), p.267-279
Main Authors: Jain, G. Pankaj, Gurupur, Varadraj P., Schroeder, Jennifer L., Faulkenberry, Eileen D.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Assisted Instruction
Concept Formation
Concept Mapping
Graphs
Learning (artificial intelligence)
Markov Processes
Probability
Probability distribution
Programming Languages
Psychology
Student Evaluation
Teaching Methods
Testing
Undergraduate Students
User interfaces
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Summary: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.
ISSN:1939-1382
1939-1382
2372-0050
DOI:10.1109/TLT.2014.2330297