Components of Educational Data Mining

Data mining has come of age. Data mining took its birth from the data which is accumulated in hundreds of thousands to millions of customer information at one location which has guaranteed consistent access and consistent storage: the data warehouse. the metaphors between data warehouse and data mining can be confusing. the philosophy that ties these two together is the data repository presented to data mining tools. Data warehousing helps you build 'data mountains', where as Data mining helps you to extract essential and vital information by building cubes, data marts from these 'data mountains' that is useful to your areas of interest.

Some of the Data Mining Algorithms includes: 

a. Knowledge Discovery

              i. Decision Trees

                        a. CART (Classification & Regression Trees)

                        b. CHAID (Chi Square Automated Initialization Decision Tree)

                 ii. Neural Networks

b. Prediction

                i. Nearest Neighbor & K-nearest neighbor

               ii. Clustering
c. Genetic Algorithms

Selecting the right data mining algorithm depending upon Model structure, Search and retrieval method and Validation requirements of the situation.

Use of LMS(Learning Management System) in Educational Data Mining

Learning management systems have made the life simpler by managing a centralized accessibility of all the requirements of an instructor, student and content management. Some of well known Learning Management System include Moodle, Sakai, WebCT, WhiteBoard, Brihaspati-3. The main aim of building a LMS is to achieve a collabrative learning atmosphere, where students can exchange there ideas, share resources and instructors can create content for the students and post at a centralized repository for the content to be available in a well organized way. some of basic qualities of a Learning Management System includes the following:

1. Course Management -course enrollment, course updates, curriculum design, types of Assignments.

2. Content Management- content design, type of content, wiki, discussion forum, groups, chats, hot potato, organize content repositories.

3. Student Management-student enrollments, profiles, student learning tools, blogs.

4. Grading and Evaluation Management- rubrics, assessment tools, grading reports, monitoring of student response, maintenance of student database repositories.

4. Institute and Administration Management- Institute authorization, institute administration, resource monitoring, database backups, recovery and restoration of content.

These are some of major functionalities most of the Learning Management system possess. So, rather than setting up individual systems for each and every functionality, all of them can be managed very well under a central repository through LMS.