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Framework for personalised online education based on learning analytics through the use of domain-specific modelling and data analytics.

Meacham, S., Nauck, D. and Zhao, H., 2019. Framework for personalised online education based on learning analytics through the use of domain-specific modelling and data analytics. In: Next Generation Computing Applications 2019, IEEE Mauritus Section, 19-21 September 2019, Mauritius, 197 - 203.

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Abstract

This paper contains a framework for the design and development of adaptive Virtual Learning Environments (VLEs) in order to assist educators of all disciplines with configuring and creating adaptive VLEs tailored to their needs. The proposed work is performed in three stages: In the first stage, development of an adaptive VLE that collects learning analytics and enables the educator to parametrize (turn on/off) their collection. The output of these analytics gets stored for further processing. In the second stage, data analysis and processing has been performed for the collected information. In the third stage, the results have been used as an input to adaptive VLE to enable informed personalisation of the student learning path and other adaptations. In this paper, we have proposed the combined use of two different environments for the different stages to achieve the most from their specialisation. For the first and the third stage, the MPS Jetbrains environment for the development of a domain-specific language (DSL) for adaptive VLEs was utilised. This development environment assists the creation of a new DSL that will enable educators to focus on the domain aspects and configure their adaptive VLE implementation to their needs. For the second stage of data analysis, the weka library was used to process the data, apply a range of classification algorithms and produce/store results that can then be used as an input to the adaptive VLE DSL. Overall, the proposed framework-system is anticipated to isolate the domain problem from the corresponding implementation details of web development and data analysis and give the adaptive VLE developer a seamless environment to experiment with a very quick turn-around time with ideas in their domain. More automation and integration between the VLE and the data science algorithms utilised for learning analytics data are part of our future plans towards the greater vision of more autonomic and personalised VLEs.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:32790
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:23 Sep 2019 08:40
Last Modified:23 Sep 2019 08:40

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