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The IUP Journal of Information Technology
Educational Data Mining: A Review
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Educational Data Mining (EDM) is a field of analysis and research where various data mining tools and techniques are used to optimize the applications in education sector. The paper presents the techniques used in EDM, the general idea and the applications of every technique. It presents some factors that affects the education sector and classifies them according to their importance and the area influence. The paper identifies all these factors and their influence on the aspects of education. Combining the tools, techniques and factors as parameters, the paper aims at developing a framework to analyze the enormous data from the education sector and provide solutions and reports for specific aspects of education sector such as student’s performance and placements. This framework will be a useful tool for educational institutions to monitor and improve their working. It is also an integrated package of applications that offers many solutions in the field of education sector.

 
 

Data mining is the process of extraction or “mining” of knowledge, i.e., useful information from enormous amounts of data (Han and Kamber, 2012). It can be interpreted as analyzing a large number of data and extracting meaningful patterns and useful information, also known as knowledge. Data mining is a well-established field of research. It is a powerful tool for areas like manufacturing industries, lie detection, corporate surveillance and many more. Data mining has proven to be an effective tool for increasing the efficiency of applications/solutions and optimization.

Education sector is one of the sectors where data mining is relatively new as compared to other sectors and hence it is under-utilized. The International Educational Data Mining Society defines EDM as follows:

“EDM is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in” (Baker, 2015).

 
 

Information Technology Journal,Education data mining, Placement analysis, Prediction