Idiosyncratic applications of data mining in E learning environments

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Idiosyncratic applications of data mining in E learning environments

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Abstract

Data Mining is a very popular and effective way of discovering new knowledge from large and complex data sets. Its benefits are its ability to gain deeper understanding of the patterns previously unseen using current available reporting capabilities. Internet education arose from traditional education in order to cover the necessities of remote students and/or help the teaching-learning process, reinforcing or replacing traditional education. In our context we call this internet education as E-Learning. In E-Learning large amounts of information describing the scale of teaching and learning interactions are generated endlessly and are universally available. By applying data mining techniques in E-learning, it would be of great use in enriching the management with precious information and knowledge that would escort to efficient decision-making. Data Mining can be used to extract knowledge from E-Learning systems through the analysis of the information available in the form of data generated by their users.

Keywords: Data Mining, E-Learning, Knowledge Management

Abstract
Introduction
E-learning
Data mining in E-learning
Applications of data mining in elearning
1. Discovering interestingness of symmetric association rules in educational data
2. Analysis of automated chat to understand and support the inquiry learning process
3. Discovering student preferences in E-learning
4. Classifying students on the basis of usage of data and their final marks
Conclusion
References

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[...] Data Mining is also used to find the patterns of system usage by teachers and students and, discover the students' learning behavior patterns APPLICATIONS OF DATA MINING IN ELEARNING Data Mining is highly contemporary to e-learning. It has most of its root in the evershifting world of business. Data Mining is not just a collection of data analysis methods, but as a data analysis process that encompasses anything from data understanding, pre-processing and modeling to process evaluation and implementation Data Mining techniques commonly bridge the fields of traditional statistics, pattern recognition and Machine Learning to provide analytical solutions to problems in areas as diverse as biomedicine, engineering, and business. [...]

[...] However, merely identifying the prospect is not enough to improve the customer value. One must somehow fit the data mining results into the execution of the content management system that enhances the profitability of customer relationships. This paper describes these kinds of distinctive applications of data mining in ELearning environments E-LEARNING The Internet and the advance of telecommunication technologies allow us to share and manipulate information in nearly real time. This reality is determining the next generation of distance education tools. [...]

[...] Results indicated that the classification of messages is reasonably reliable and could be done automatically and in real-time. This made it possible to increase the awareness of learners by visualizing their interaction behavior by means of avatars. Finally they have concluded that application of data mining methods to educational chat is feasible and can result in the improvement of learning environments DISCOVERING STUDENT PREFERENCES IN E-LEARNING Nowadays modeling users' preferences is one of the most challenging tasks in E-Learning systems that deal with large volumes of information The growth of online educational resources including encyclopedias, repositories, etc. [...]

[...] A major component to the whole content management system is the data mining applications which enable managerial levels to track, understand, and manage the wealth of information stored in multiple data sources. Data Mining tools when used on ELearning databases, show impressive accurate real time results. With this concern, we are forced to consider, the applications of data mining in ELearning in the same way as the content because the quality is a major concern that requires special handling before uploading the data or getting started with the building of the models REFERENCES Vranic, M. [...]

[...] This way the researchers could obtain a new labeled example that can be used to adapt both the learning style model and the decision model, accordingly CLASSIFYING STUDENTS ON THE BASIS OF USAGE OF DATA AND THEIR FINAL MARKS. Here the researchers have compared different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses They have developed a specific mining tool for making the configuration and execution of data mining techniques easier for instructors. [...]

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