Educational data mining (EDM): Researching impact on online business education

Kriti Khare, Helen Lam, Anshuman Khare

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Citations (Scopus)


Educational data mining (EDM) is an applied field of research that combines data mining, machine learning, and statistics in the educational setting at, but not limited to, schools, universities and intelligent tutoring systems, and MOOCs. Methods are developing and improving for analyzing the vast amount of data available in education to better understand learning behaviors and pedagogical outcomes by applying theories of educational psychology in order to improve the learning environment. In this chapter, the authors explore the significance of data mining in the online education setting and how it can improve the student learning experience. We first review some commonly used data mining techniques that have been applied to education data, such as classification, clustering, and association rule mining. The new developments in knowledge tracing for modeling student data are also briefly described. We follow with an elaboration on the goals of educational data mining, for example, using the data related to student performance in an MOOC setting, some goals for data mining could be to predict how well the student will do on a new class activity or the probability of the student dropping the course. And we conclude with a look at how EDM applies to online business education and how learning systems can adapt to findings of this data analysis.

Original languageEnglish
Title of host publicationOn the Line
Subtitle of host publicationBusiness Education in the Digital Age
Number of pages17
ISBN (Electronic)9783319627762
Publication statusPublished - 13 Sep. 2017


  • Data analysis
  • Educational data mining
  • Online business learning environment


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