A Systematic Review on Educational Data Mining

Ashish Dutt, Maizatul Akmar Ismail, Tutut Herawan

Research output: Contribution to journalReview ArticleResearchpeer-review

353 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)15991-16005
Number of pages15
JournalIEEE Access
Publication statusPublished - 2017
Externally publishedYes


  • clustering methods
  • Data mining
  • educational technology
  • systematic review

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