The Artificial Intelligence in the Support of e-Learning Management and Quality Maintenance

The Artificial Intelligence in the Support of e-Learning Management and Quality Maintenance

Wieslaw Pietruszkiewicz, Dorota Dzega
Copyright: © 2012 |Pages: 23
DOI: 10.4018/978-1-4666-0939-6.ch006
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Abstract

This chapter presents how Artificial Intelligence (AI) could be used to support the management of e-learning platforms and maintenance of e-learning qualities. Two AI applications in e-learning presented herein will be analyzed. The first one evaluates Learning Management System users. It was tested on a real life situation for blended-learning process and could be used to support the management of e-learning or further extended to any virtual community, where team members cooperate as a geographically distributed team. The second application analyzes adaptive courses and their features that should be perceived from Web 3.0 perspective. The chapter addresses features relating to the quality of e-learning and how Artificial Intelligence helps to maintain the high quality for e-learning content. For more complex and advanced learning processes AI is analyzed in a manner to reveal its potential to be a solution to educational systems where increasing demands for quantity and quality cause that managerial issues could be efficiently coped only via the intelligent supporting software systems.
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Applications Of Artificial Intelligence In E-Learning

The application of Artificial Intelligence and Data Mining (they share many the same methods, thus we will mainly use the term of AI later) in e-learning have a similar history as e-learning itself. Every day Learning Management Systems (LMS) generate a lot of data about e-learning users, their actions and behaviors. An analysis of these huge data sets using only human power is not a trivial task. Baker and Yacef (2009) analyzed the popularity of AI & DM methods in research done over distance learning. Research sample contained papers involving each type of EDM (Educational Data Mining) method in the proceedings of Educational Data Mining 2008 and 2009. The results of their research are as follows:

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    Relationship mining: 9%,

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    Clustering: 15%,

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    Prediction: 42%,

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    Human judgment/exploratory data analysis: 12%,

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    Discovery with model: 19%,

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    None of the above: 28%.

The numbers do not add-up to 100%, because some papers used multiple methods and these papers were counted toward multiple categories.

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