A Model-Driven Approach to Developing Scalable Educational Software for Adaptive Learning Environments
DOI:
https://doi.org/10.33050/itee.v3i1.663Keywords:
Artificial Intelligence, Education Software, Learning Environments, Digital toolAbstract
This research presents a model-driven approach to the development of scalable educational software tailored to adaptive learning environments. With the increasing demand for personalized education, adaptive learning systems play a crucial role in meeting diverse student needs by adjusting instructional content dynamically. This paper proposes a software engineering framework that integrates model-driven development (MDD) techniques with scalability principles, allowing for the efficient design and implementation of educational applications that can handle varying workloads and user demands. The framework emphasizes modular architecture, reusability, and flexibility to ensure that software can evolve with emerging educational requirements. Key components include the design of a learning content management system (LCMS) and the application of adaptive algorithms to personalize learning pathways. Additionally, this study explores the integration of cloud technologies to enhance the scalability and performance of educational platforms. A prototype system was developed and tested in a controlled environment, showing significant improvements in scalability, system performance, and student engagement compared to traditional static e-learning platforms. The results indicate that the model-driven approach not only improves software development efficiency but also offers a robust solution for creating adaptive educational systems that can scale to meet the growing needs of learners and institutions. This research contributes to the field of educational software development by providing a systematic methodology for building scalable and adaptive learning environments using advanced software engineering techniques.
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