Resource description framework based methodology to personalise learning
Irina Krikun
Vilniaus universitetas
Eugenijus Kurilovas
Vilniaus Gedimino technikos universitetas
Published 2016-12-20


educational data mining
learning analytics
learning personalisation
systematic literature review
personalised recommendations

How to Cite

Krikun I. and Kurilovas E. (2016) “Resource description framework based methodology to personalise learning”, Lietuvos matematikos rinkinys, 57(B), pp. 25–30. doi: 10.15388/LMR.B.2016.05.


The paper aims to analyse Educational Data Mining/Learning Analytics application trends to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying educational data mining methods to personalise learning were identified. Second, three main tendencies on educational data mining/learning analytics application in education were formulated. They are: (a) Educational Data Mining/Learning Analytics support self-directed autonomous learning; (b) Educational Data Mining/Learning Analytics systems become essential tools of educational management; and (c) most teaching is delegated to computers, and Educational Data Mining/Learning Analytics based recommendations become better and more reliable than those that can be produced by even the best-trained teachers.

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