Educational data mining to inspect low academic performance areas of the students using ensemble classification

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Khawar Shakeel

University of Gujrat, Pakistan

: J Comput Eng Inf Technol

Abstract

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Educational Data Mining (EDM) is a topical region these days with many areas to be researched. It supports in decision making by applying Data Mining (DM) techniques on education related data to deal with matters that would be inflexible without them. These techniques try to extract valuable patterns that may lead to strategic policy making and to determine the behaviors of both, the teacher and student from educational point of view. Therefore, this evidence will lead us on the way to the finding of which approaches must be avoided, which and how teaching tactics can be improved to each group of students or to expect which students will perform good or poor so that specific group of students can be facilitated timely on initial stage. Administration might rapidly be capable to practice this innovative information as directions for course redesign and as indication for executing new assessments criteria. Technically, the study found a bit higher accuracy by using boosting ensemble technique as compared to bagging, the execution of the perposed model confirmed the claim.

Biography

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Khawar Shakeel has completed his Master of Science and Master of Philosphy in Computer Science from a University of Gujrat. He has served University of Gujrat as Computer Programmer and Database Admininstrator. He is a Research Student and his area of interest is Machine Learning, Data Mining and Information Retrival.

Email: khawarshakeel@gmail.com