
Resource description framework based methodology to personalise learning
Author(s) -
Irina Krikun,
Eugenijus Kurilovas
Publication year - 2016
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.b.2016.05
Subject(s) - learning analytics , educational data mining , analytics , computer science , data science , data analysis , resource (disambiguation) , learning management , software analytics , knowledge management , data mining , world wide web , software , computer network , software construction , software system , programming language
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.