Educational Data Mining - Challenges and Opportunities in Global Scenario

Educational data mining is concerned with developing methods that discover knowledge from data that come from educational environment. The data can be collected from historical and operational data reside in the databases of educational institutes. It can also be collected from e-sources which has a vast amount of information used by most institutes. Educational data mining used many techniques such as decision trees, neural networks, K-nearest Neighbor, Naive Bayes and support vector machines. Using these methods many kinds of knowledge can be discovered such as association rules, classifications, clustering and outlier detection. The discovered knowledge can be used to better understand students' behavior, to assist instructors to improve teaching, to evaluate and improve e-learning systems, to improve curriculums and many other benefits in the global scenario.