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

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

Ali Fardinpour, Mir Mohsen Pedram, Martha Burkle
Copyright: © 2014 |Volume: 12 |Issue: 4 |Pages: 13
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781466653542|DOI: 10.4018/ijdet.2014100102
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MLA

Fardinpour, Ali, et al. "Intelligent Learning Management Systems: Definition, Features and Measurement of Intelligence." IJDET vol.12, no.4 2014: pp.19-31. http://doi.org/10.4018/ijdet.2014100102

APA

Fardinpour, A., Pedram, M. M., & Burkle, M. (2014). Intelligent Learning Management Systems: Definition, Features and Measurement of Intelligence. International Journal of Distance Education Technologies (IJDET), 12(4), 19-31. http://doi.org/10.4018/ijdet.2014100102

Chicago

Fardinpour, Ali, Mir Mohsen Pedram, and Martha Burkle. "Intelligent Learning Management Systems: Definition, Features and Measurement of Intelligence," International Journal of Distance Education Technologies (IJDET) 12, no.4: 19-31. http://doi.org/10.4018/ijdet.2014100102

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Abstract

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.

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