Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic
| Autor: | Eileen D. Faulkenberry, Varadraj P. Gurupur, Jennifer L. Schroeder, G. Pankaj Jain |
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| Rok vydání: | 2014 |
| Předmět: |
Parsing
business.industry Computer science computer.internet_protocol Concept map Teaching method General Engineering Computer-Assisted Instruction computer.software_genre Machine learning Computer Science Applications Education Concept learning ComputingMilieux_COMPUTERSANDEDUCATION Probability distribution Artificial intelligence User interface business computer XML |
| Zdroj: | IEEE Transactions on Learning Technologies. 7:267-279 |
| ISSN: | 1939-1382 |
| DOI: | 10.1109/tlt.2014.2330297 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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