Search icone
Search and publish your papers

Idiosyncratic applications of data mining in E learning environments

Or download with : a doc exchange

About the author

Level
Expert

About the document

Published date
Language
documents in English
Format
pdf
Type
term papers
Pages
5 pages
Level
Expert
Accessed
0 times
Validated by
Committee Oboolo.com
0 Comment
Rate this document
  1. Abstract
  2. Introduction
  3. E-learning
  4. Data mining in E-learning
  5. 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
  6. Conclusion
  7. References

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

[...] 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. [...]


[...] Databases store the information per learning provider like name of the company, type of the company, size of the company, courses provided, number of courses, areas/subjects of courses, targeted learners (audience), number of hours (duration) per course, cost, prerequisites of each course, course objectives, contents, course path, etc DATA MINING IN E-LEARNING The extraction of useful and non-trivial information from the huge amount of data that is possible to collect in many and diverse fields of science, business and engineering, is called Data Mining (DM). [...]

Recent documents in computer science category

Net neutrality in United States

 Science & technology   |  Computer science   |  Presentation   |  10/02/2018   |   .doc   |   3 pages

Reconstructing householder vectors from tall-skinny QR

 Science & technology   |  Computer science   |  Presentation   |  04/21/2017   |   .doc   |   4 pages