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

• Published in: Telematics and informatics Vol. 37; pp. 13 - 49
• Main Authors: Aldowah, Hanan, Al-Samarraie, Hosam, Fauzy, Wan Mohamad
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2019; Elsevier Science Ltd
• Subjects: Analytics; CAI; Computer assisted instruction; Continuous improvement; Data analysis; Data analytics; Data mining; Education; Educational data mining; Higher education; Learning; Learning analytics; Mathematical analysis; Educational data mining; Learning analytics; Data analytics; Higher education
• ISSN: 0736-5853; 1879-324X
• DOI: 10.1016/j.tele.2019.01.007

•A review of educational data mining (EDM) and learning analytics (LA) in higher education was conducted.•Four main dimensions related to learning, predictive, behavioral, and visualization were identified.•The utilization of data mining techniques across these dimensions was mapped. 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.