Journal
TELEMATICS AND INFORMATICS
Volume 37, Issue -, Pages 13-49Publisher
ELSEVIER
DOI: 10.1016/j.tele.2019.01.007
Keywords
Dta analytics; Educational data mining; Learning analytics; Higher education
Categories
Funding
- Universiti Sains Malaysia
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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.
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