Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Hanan Aldowah, Hosam Al-Samarraie, Wan Mohamad Fauzy

Research output: Contribution to journalReview articlepeer-review

313 Citations (Scopus)


The potential influence of data mining analytics on the students’ learning processes and outcomes has been realized in higher education. Hence, a comprehensive review of educational data mining (EDM) and learning analytics (LA) in higher education was conducted. This review covered the most relevant studies related to four main dimensions: computer-supported learning analytics (CSLA), computer-supported predictive analytics (CSPA), computer-supported behavioral analytics (CSBA), and computer-supported visualization analytics (CSVA) from 2000 till 2017. The relevant EDM and LA techniques were identified and compared across these dimensions. Based on the results of 402 studies, it was found that specific EDM and LA techniques could offer the best means of solving certain learning problems. Applying EDM and LA in higher education can be useful in developing a student-focused strategy and providing the required tools that institutions will be able to use for the purposes of continuous improvement.

Original languageEnglish
Pages (from-to)13-49
Number of pages37
JournalTelematics and Informatics
Early online date14 Jan 2019
Publication statusPublished - 1 Apr 2019
Externally publishedYes


  • Data analytics
  • Educational data mining
  • Higher education
  • Learning analytics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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