Paper Info
Title
Advanced Machine Learning Approaches To Personalise Learning: Learning Analytics And Decision Making
Abstract
The aim of the paper is to present methodology to personalise learning using learning analytics and to make further decisions on suitability, acceptance and use of personalised learning units. In the paper, first of all, related research review is presented. Further, an original methodology to personalise learning applying learning analytics in virtual learning environments and empirical research results are presented. Using this learning personalisation methodology, decision-making model and method are proposed to evaluate suitability, acceptance and use of personalised learning units. Personalised learning units evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify learning components' suitability to particular students' needs according to their learning styles. In the paper, there are also examples of implementing the methodology using different weights of evaluation criteria. This methodology is applicable in real life situations where teachers have to help students to create and apply learning units that are most suitable for their needs and thus to improve education quality and efficiency.
Year
DOI
Venue
2019
10.1080/0144929X.2018.1539517
BEHAVIOUR & INFORMATION TECHNOLOGY
Keywords
Field
DocType
Learning analytics, decision making, personalisation, learning units, evaluation of suitability, acceptance and use
Data science,Learning analytics,Computer science,Knowledge management,Personalization
Journal
Volume
Issue
ISSN
38
4
0144-929X
Citations 
PageRank 
References 
0
0.34
7
Authors
1
Name
Order
Citations
PageRank
Eugenijus Kurilovas19815.53