Skip to main content

AdaptiveVLE: an integrated framework for personalised online education using MPS JetBrains domain-specific modelling environment.

Meacham, S., Pech, V. and Nauck, D., 2020. AdaptiveVLE: an integrated framework for personalised online education using MPS JetBrains domain-specific modelling environment. IEEE Access, 8, 184621 -184632.

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
ACCESS3029888.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

DOI: 10.1109/ACCESS.2020.3029888

Abstract

This paper contains the design and development of an Adaptive Virtual Learning Environment (AdaptiveVLE) framework to assist educators of all disciplines with creating adaptive VLEs tailored to their needs and to contribute towards the creation of a more generic framework for adaptive systems. Fully online education is a major trend in education technology of our times. However, it has been criticised for its lack of personalisation and therefore not adequately addressing individual students’ needs. Adaptivity and intelligence are elements that could substantially improve the student experience and enhance the learning taking place. There are several attempts in academia and in industry to provide adaptive VLEs and therefore personalise educational provision. All these attempts require a multiple-domain (multi-disciplinary) approach from education professionals, software developers, data scientists to cover all aspects of the system. An integrated environment that can be used by all the multiple-domain users mentioned above and will allow for quick experimentation of different approaches is currently missing. Specifically, a transparent approach that will enable the educator to configure the data collected and the way it is processed without any knowledge of software development and/or data science algorithms implementation details is required. In our proposed work, we developed a new language/framework using MPS JetBrains Domain-Specific Language (DSL) development environment to address this problem. Our work consists of the following stages: data collection configuration by the educator, implementation of the adaptive VLE, data processing, adaptation of the learning path. These stages correspond to the adaptivity stages of all adaptive systems such as monitoring, processing and adaptation. The extension of our framework to include other application areas such as business analytics, health analytics, etc. so that it becomes a generic framework for adaptive systems as well as more usability testing for all applications will be part of our future work.

Item Type:Article
ISSN:2169-3536
Uncontrolled Keywords:DS , Adaptive systems, Education, Adaptation models, Data collection, Classification algorithms
Group:Faculty of Science & Technology
ID Code:34707
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:19 Oct 2020 08:09
Last Modified:23 Nov 2020 14:25

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -