Association Rules Mining from the Educational Data of ESOG Web-Based Application

نویسندگان

  • Stefanos Ougiaroglou
  • Giorgos Paschalis
چکیده

Many researchers have focused on the mining of educational data stored in databases of educational software and Learning Management Systems. The goal is the knowledge discovery that can help educators to support their students by managing effectively educational units, redesigning student’s activities and finally improving the learning outcome. A basic data mining technique concerns the discovery of hidden associations that exist in data stored in educational software Databases. In this paper, we present the KDD process which includes the application of the Apriori algorithm for the association rules mining from the educational data of ESOG Web-based application.

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تاریخ انتشار 2012