Intelligent Learning Management Systems: Definition, Features and Measurement of Intelligence

Virtual Learning Environments have been the center of attention in the last few decades and help educators tremendously with providing students with educational resources. Since artificial intelligence was used for educational proposes, learning management system developers showed much interest in m...

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Bibliographic Details
Published in:International journal of distance education technologies 2014-10, Vol.12 (4), p.19-31
Main Authors: Fardinpour, Ali, Pedram, Mir Mohsen, Burkle, Martha
Format: Article
Language:English
Subjects:
Analysis
Analytic hierarchy process
Artificial Intelligence
CAI
Computer assisted instruction
Customization
Decision Making
Education
Educational Resources
Evaluation
Evaluation Criteria
Fuzzy
Fuzzy logic
Fuzzy set theory
Integrated Learning Systems
Intelligence
Learning
Learning management systems
Management Systems
Product design
Reliability
Virtual environments
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Summary:Virtual Learning Environments have been the center of attention in the last few decades and help educators tremendously with providing students with educational resources. Since artificial intelligence was used for educational proposes, learning management system developers showed much interest in making their products smarter and more intelligent. Nevertheless, the questions of what an intelligent learning management system (ILSM) is and which tools and features are needed to make such system intelligent, are not clearly answered, therefore educational institutes do not have a proper tool to decide upon the degree of intelligence they need for their LMSs. This paper proposes a prevalent, thorough definition of “Intelligent Learning Management Systems”, and the design of a fuzzy model to measure the intelligence of these systems. In order to devise a comprehensive definition of an Intelligent Learning Management System, experts from around the world were consulted. Following that, different proposed Intelligent Learning Management Systems were studied, and forty-one features and tools were found and analyzed. After the analysis, experts' opinions were taken into account to rank these features. The paper proposes thirteen most significant features and tools as criteria to be used in fuzzy analytic hierarchy process (AHP) as a fuzzy model to measure the intelligence of Learning Management System.
ISSN:1539-3100
1539-3119
DOI:10.4018/ijdet.2014100102