About this paper

Appears in:
Pages: 141-148
Publication year: 2017
ISBN: 978-84-697-3777-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2017.1033

Conference name: 9th International Conference on Education and New Learning Technologies
Dates: 3-5 July, 2017
Location: Barcelona, Spain

APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO SUPPORT PERSONALISED LEARNING

E. Kurilovas

Vilnius Gediminas Technical University, Vilnius University Institute of Mathematics and Informatics (LITHUANIA)
The paper aims to analyse possible application of artificial neural networks (ANN) to support learning personalisation and optimisation in terms of enhancing learning quality and effectiveness. ANN are referred here as a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. Information that flows through the network affects the structure of the ANN because a neural network changes – or learns, in a sense – based on that input and output. An ANN has several advantages but one of the most recognised of these is the fact that it can actually learn from observing data sets. In the paper, first of all, systematic review was performed in Clarivate Analytics (formerly Thomson Reuters) Web of Science database. The following research question has been raised to perform systematic literature review: “What are existing ANN methods, tools, and techniques applied to support personalised learning?” During XXI century (2001-2017), 100 articles in English were found in Web of Science database on the topic “TS=(artificial neural network* AND education)”. After applying Kitchenham’s systematic review methodology, on the last stage 20 suitable articles were identified to further detailed analysis on possible application of ANN to support personalised learning both in general and Higher education. Systematic review has shown that ANN are already quite actively used in both school and University education to solve different problems e.g. academic assessment, predicting students’ success and dropout, predicting instructional effectiveness of virtual learning environments, performance evaluation of online teaching and learning, improving students’ motivation, analysing emotional social and cognitive competencies, modelling student cognitive processes, cognitive diagnostic, course timetabling etc. At the same time, ANN are still rarely used to personalise learning according to students’ needs, and future research is needed in the area. After that, the author’s original learning personalisation methodology based on identifying students’ learning styles and other needs is presented in more detail. The last but not the least, some insights on possible application of ANN to support personalised learning are provided. This should be helpful to enhance learning quality and effectiveness.
@InProceedings{KURILOVAS2017APP,
author = {Kurilovas, E.},
title = {APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO SUPPORT PERSONALISED LEARNING},
series = {9th International Conference on Education and New Learning Technologies},
booktitle = {EDULEARN17 Proceedings},
isbn = {978-84-697-3777-4},
issn = {2340-1117},
doi = {10.21125/edulearn.2017.1033},
url = {http://dx.doi.org/10.21125/edulearn.2017.1033},
publisher = {IATED},
location = {Barcelona, Spain},
month = {3-5 July, 2017},
year = {2017},
pages = {141-148}}
TY - CONF
AU - E. Kurilovas
TI - APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO SUPPORT PERSONALISED LEARNING
SN - 978-84-697-3777-4/2340-1117
DO - 10.21125/edulearn.2017.1033
PY - 2017
Y1 - 3-5 July, 2017
CI - Barcelona, Spain
JO - 9th International Conference on Education and New Learning Technologies
JA - EDULEARN17 Proceedings
SP - 141
EP - 148
ER -
E. Kurilovas (2017) APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO SUPPORT PERSONALISED LEARNING, EDULEARN17 Proceedings, pp. 141-148.
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