Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic
Abbreviated Journal Title
IEEE Trans. Learn. Technol.
Concept maps; evaluation; probability distributions; XML parsers; PROBABILITY DENSITY-FUNCTION; MARKOV-CHAINS; KNOWLEDGE; Computer Science, Interdisciplinary Applications; Education &; Educational Research
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.
Ieee Transactions on Learning Technologies
"Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic" (2014). Faculty Bibliography 2010s. 5501.