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

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

Ali Fardinpour (School of Information Systems, Curtin University, Bentley, WA, Australia), Mir Mohsen Pedram (Department of Computer Engineering, Kharazmi University, Tehran, Iran) and Martha Burkle (Center for Distance Education, Athabasca University, Athabasca, Canada)
Copyright: © 2014 |Pages: 13
DOI: 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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1. Introduction

When analyzing an Intelligent Learning Management System (iLMS), the most crucial issue is to be sure that there is a common understanding and definition of such a system. Since the first proposed Intelligent Learning Management System, there is not a clear definition of what an Intelligent Learning Management System would be, and what the main difference between and iLMS and Learning Management System is. Many researchers or education technology companies have named their Learning Management Systems “Intelligent” but determining how smart these systems are, and which features make them smart is the main aim of this contribution.

This paper aims to propose a prevalent and thorough definition of an Intelligent Learning Management System based on the definition and understanding of experts in e-learning, learning technologies and Internet technologies. Furthermore, other than a commonly accepted notion knowing its main features and tools is also crucial. All different Intelligent Learning Management Systems currently available were studied, and forty-one tools and features were distinguished within the research survey while some of them were just the same in function but have different names. Among them, experts identified thirteen most important features and tools as the key criteria, which every Intelligent Learning Management System should be equipped with. Once those criteria were defined, we asked the experts of Intelligent Learning Management System, Education Technology and Internet Technology to compare those measurements by a nine-point ratio measurement scale developed by Saaty (2008). Finally, a fuzzy model was proposed to measure the degree of intelligence of any Intelligent Learning Management System based on multi criteria decision making (MCDM). The paper proposed that by following this model, which is a fuzzy analytic hierarchy process (AHP), prospective clients would be able to rank their choices and measure their intelligence. Saaty (2008) defines the Analytic Hierarchy Process (AHP) as “a theory of measurement through pairwise comparisons and relies on the judgments of experts to derive priority scales. It is these scales that measure intangibles in relative terms.”

In the following sections, we will review the current proposed Intelligent Learning Management Systems and based on experts’ opinions a new definition will be proposed. Finally, the steps of designing a fuzzy model to measure the intelligence of Learning Management System will be explained.

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