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Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic
Open accessAbstract
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.
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The intelligent reasoning system can be used as a form of evaluation to assess if the student has captured enough concepts for the given topic.
“In return, the intelligent reasoning system can be used as a form of evaluation to assess if the student has captured enough concepts for the given topic [41].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [41]
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Sorted by citations
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
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“Jain et al. [41] combined visualized mapping tool with collaboration scripts. design successfully helped learners to analyze and evaluate opposing positions on contentious topics.”
Background
Metadata
- Authors
- Jain, G. P., Gurupur, V. P., Schroeder, J. L., & Faulkenberry, E. D.
- Affiliations
- East Texas A&M University (US)
- University of Central Florida (US)
- ORCID
- Gurupur, Varadraj P.
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2014-07-01
- Volume
- 7
- Issue
- 3
- Pages
- 267-279
- ISSN
- 1939-1382 (ISSN-L)
- OpenAlex
- W2037609558
- Cited by
- 58
- References
- 40
- Citations by year
- 2025: 4 · 2024: 9 · 2023: 7 · 2022: 8 · 2021: 9 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 5 · 2016: 3 · 2015: 2 · 2014: 1
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion)
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- student learning evaluation, concept maps, XML parsing, educational technology tools, undergraduate students
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