An MDA-based Approach for the Design and Automatic Computation of Collaboration Indicators in E-Learning Systems
Received: 18 February 2025 | Revised: 3 April 2025 | Accepted: 9 April 2025 | Online: 27 April 2025
Corresponding author: Sara Ghaoui
Abstract
Effectively assessing collaboration in e-learning environments requires robust indicators that capture learner interactions. However, most existing approaches are platform-dependent, rely on predefined indicators, and offer little flexibility for tutors and teachers. This limits their ability to tailor assessments to specific learning contexts. To address these challenges, this study proposes a Model-Driven Architecture (MDA)-based approach that allows tutors and teachers to design and compute collaboration indicators independently of any specific platform, without requiring advanced technical expertise. This approach introduces a flexible and scalable framework for the design and computation of collaboration indicators in e-learning analytics. First, it ensures platform-independent computation by leveraging a model transformation approach within an MDA-based process. Second, it provides a formal model that enables teachers and tutors to define meaningful and adaptable indicators that align with their specific observation needs. Finally, the proposed DECIN-AGST system automates the generation of transformation sequences. The accuracy and reliability of the proposed system were validated through formal verification using the UPPAAL model checker.
Keywords:
collaboration indicator, e-learning system, sequence of transformations, MDA, learning assessment, e-learning analyticsDownloads
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Copyright (c) 2025 Sara Ghaoui, Sofiane Mounine Hemam, Tarek Djouad
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