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Home / programming language / Online Java Projects / A Systematic Review on Educational Data Mining
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A Systematic Review on Educational Data Mining

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SKU: PROJ7612 Categories: 2017 Projects, Datamining Projects, Final Year Projects, Online Java Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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A Systematic Review on Educational Data Mining

Abstract— Presently educational institutions compile and store huge volumes of data such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of Educational Data Mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is Clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified. Various methods have been proposed, applied and tested in the field of EDM. It is argued that these generic methods or algorithms are not suitable to be applied to this emerging discipline. It is proposed that EDM methods must be different from the standard DM methods due to the hierarchical and non-independent nature of educational data < final year projects >

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