Data Mining Applications: A comparative Study for Predicting Student's performance

Abstract

Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.

7 pages, 2 figures. arXiv admin note: text overlap with arXiv:1201.3417 and arXiv:1201.3418

References in corpus (3)

• Data Mining: A prediction for performance improvement using classification
• Data Mining : A prediction of performer or underperformer using classification
• A Data Mining view on Class Room Teaching Language

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• Student Dropout Risk Assessment in Undergraduate Course at Residential University
• Data Mining Application to Attract Students in HEI
• Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques