%0 Journal Article %1 educational %A "Lakshmi Prabha, S" %A "Mohamed Shanavas, A.R" %D 2014 %J Operations Research and Applications: An International Journal (ORAJ) %K Data E-Learning Educational Mining Profiling Trend User analysis and domain modeling %N 1 %P 23-29 %R 10.5121/oraj.2014.1104 %T EDUCATIONAL DATA MINING APPLICATIONS %U http://airccse.com/oraj/papers/1114oraj04.pdf %V 1 %X The tools and techniques of data mining are being adopted by the industries to generate business intelligence for improving decision making. Education institutions are beginning to use data mining techniques for improving the services they provide and for increasing student grades and retention. This paper presents broad areas of applications in which educational data mining can be applied to e-learning. The application areas discussed in this paper are: 1) User modelling 2) User grouping or profiling 3) Domain modelling and 4) Trend analysis. A MathsTutor for school students of 6th, 7th and 8th grade is designed and implemented in 3 schools in Tamilnadu. The student data are taken for analysis for the above said areas. This helps teachers, policy makers, and administrators to understand how educational data mining work to support education related decision making

@article{educational, abstract = {The tools and techniques of data mining are being adopted by the industries to generate business intelligence for improving decision making. Education institutions are beginning to use data mining techniques for improving the services they provide and for increasing student grades and retention. This paper presents broad areas of applications in which educational data mining can be applied to e-learning. The application areas discussed in this paper are: 1) User modelling 2) User grouping or profiling 3) Domain modelling and 4) Trend analysis. A MathsTutor for school students of 6th, 7th and 8th grade is designed and implemented in 3 schools in Tamilnadu. The student data are taken for analysis for the above said areas. This helps teachers, policy makers, and administrators to understand how educational data mining work to support education related decision making}, added-at = {2018-07-10T14:35:37.000+0200}, author = {"Lakshmi Prabha, S" and "Mohamed Shanavas, A.R"}, biburl = {https://www.bibsonomy.org/bibtex/2a1866f38ae63e966bc83ed4e5378583c/oraj}, doi = {10.5121/oraj.2014.1104}, interhash = {5ba2cfb3ad7000a3d4b1dd0c4199373a}, intrahash = {a1866f38ae63e966bc83ed4e5378583c}, journal = {Operations Research and Applications: An International Journal (ORAJ)}, keywords = {Data E-Learning Educational Mining Profiling Trend User analysis and domain modeling}, month = {August}, number = 1, pages = {23-29}, timestamp = {2018-07-10T14:35:37.000+0200}, title = {EDUCATIONAL DATA MINING APPLICATIONS}, url = {http://airccse.com/oraj/papers/1114oraj04.pdf}, volume = 1, year = 2014 }