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1. Sofia Meacham, Vaclav Pech, Detlef Nauck

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


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.

IEEE AccessRead paper

2. Andreas Prinz, Gergely Mezei

The Art of Bootstrapping


Language workbenches are used to define languages using appropriate meta-languages. Meta-languages are also just languages and can, therefore, be defined using themselves. The process is called bootstrapping and is often difficult to achieve. This paper compares four different bootstrapping solutions. The EMF environment and the Meta-Programming System (MPS) use a compiled bootstrapping for their own definition. The platforms LanguageLab and DMLA are using interpreted bootstrapping. This paper compares these kinds of bootstrapping and relates them to the definition of instantiation. Besides the structural aspects of the bootstraps, the dynamism is also elaborated. It is shown how the bootstrap is related to the execution environment. Finally, the level of changeability is also discussed. It is shown that all approaches are quite similar and provide very flexible environments.

MODELSWARD 2019. Communications in Computer and Information Science, vol 1161. Springer, ChamLink

3. Sofia Meacham, Vaclav Pech, Detlef Nauck

Classification Algorithms Framework (CAF) to Enable Intelligent Systems Using JetBrains MPS Domain-Specific Languages Environment


IEEE Access Volume: 8 ), Read paper


1. Andreas Prinz, Alexander Shatalin

How to Bootstrap a Language Workbench


Language workbenches are designed to enable the definition of languages using appropriate meta-languages. This makes it feasible to define the environments by themselves, as the meta-languages are also just languages. This approach of defining an environment using itself is called bootstrapping. Often, such bootstrapping is difficult to achieve and has to be built deeply into the environment. The platform Meta-Programming System (MPS) has used bootstrapping for its own definition. In a similar way, the environment LanguageLab is using bootstrapping for its definition. This paper reports the implementation of LanguageLab in MPS thereby also porting the bootstrapping. From the experiences general requirements for bootstrapping language workbenches are derived.

MODELSWARD 2019: Proceedings of the 7th International Conference on Model-Driven Engineering and Software DevelopmentLink

2. M. Voelter, K. Birken, S. Lisson, A. Rimer

Shadow Models - Incremental Transformations for MPS