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 (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.
Highlights
It has been recognized that the systems of the future in several application contexts will contain in some form or another big data gathering and analysis
We have developed an integrated framework to mainly assist educators that develop purely online courses utilizing learning analytics in a systematic and ‘‘domain-specific’’ way using domain-specific languages (DSLs) [4]
In our proposed environment an interface is provided for the educator to configure the data that will be collected, another interface in the same environment to run a classification algorithm from a choice of algorithms using our previously published Classification Algorithms Framework (CAF) DSL [5] and produce the results in the same screen, adapt the learning path for each student according to these results
Summary
It has been recognized that the systems of the future in several application contexts will contain in some form or another big data gathering and analysis. Data for learning evidence consist of information collected from these VLEs from student grades to the number of accesses to resources Utilizing these data to provide personalization and enhanced student experience is a promising area and especially for purely online education. We have developed an integrated framework (extending the work published in our previous conference paper [3]) to mainly assist educators that develop purely online courses utilizing learning analytics in a systematic and ‘‘domain-specific’’ way using domain-specific languages (DSLs) [4]. This framework consists of two main DSLs: AdaptiveVLE DSL and Classification Algorithms Framework (CAF) DSL.
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