AdaptiveVLE: An integrated framework for personalized online education using MPS jetbrains domain-specific modeling environment
Authors: Meacham, S., Pech, V. and Nauck, D.
Journal: IEEE Access
Volume: 8
Pages: 184621-184632
eISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3029888
Abstract:This article 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 (multidisciplinary) 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 DomainSpecific 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.
https://eprints.bournemouth.ac.uk/34707/
Source: Scopus
AdaptiveVLE: An Integrated Framework for Personalized Online Education Using MPS JetBrains Domain-Specific Modeling Environment
Authors: Meacham, S., Pech, V. and Nauck, D.
Journal: IEEE ACCESS
Volume: 8
Pages: 184621-184632
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3029888
https://eprints.bournemouth.ac.uk/34707/
Source: Web of Science (Lite)
AdaptiveVLE: an integrated framework for personalised online education using MPS JetBrains domain-specific modelling environment
Authors: Meacham, S., Pech, V. and Nauck, D.
Journal: IEEE Access
Publisher: IEEE
ISSN: 2169-3536
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.
https://eprints.bournemouth.ac.uk/34707/
Source: Manual
AdaptiveVLE: an integrated framework for personalised online education using MPS JetBrains domain-specific modelling environment
Authors: Meacham, S., Pech, V. and Nauck, D.
Journal: IEEE Access
Volume: 8
Pages: 184621-184632
ISSN: 2169-3536
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.
https://eprints.bournemouth.ac.uk/34707/
Source: BURO EPrints