# Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review
###### Abstract  
Adaptive Mobile Learning Systems (AMLSs) are technological tools that incorporate adaptive learning to generate an individual teaching-learning process for each student. Despite the proliferation of AMLS, their development is often hindered by a lack of standardization and the poor quality of existing software architectures in terms of requirements correctness and coverage. This paper presents a systematic literature review (SLR) of software architectures oriented to developing AMLS reported in the state of the art. For this SLR, we searched the ACM Digital Library, IEEE Xplore, Science Direct, Springer, and SCOPUS. Studies published in English that describe and evaluate a software architecture to develop AMLS were included. Twenty-two primary works were relevant to the present study. Based on the obtained results, we have identified key aspects that must be considered in a software architecture proposed for developing an AMLS in terms of requirements, quality attributes, stakeholders, services provided to users, views and architectural styles, components, and implementation technologies. The main finding of this work is the lack of a reference software architecture that considers all the requirements and quality attributes needed for developing AMLS. This gap hinders the effective development and standardization of quality AMLS, suggesting a critical need for a comprehensive reference software architecture. software architectures; adaptive mobile learning systems; requirements; architectural design; evaluation methods +
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works [5; 6; 7; 8] to propose systems that implement an adaptive learning approach to (a) dynamically adjust the teaching-learning process to the characteristics of the students considering their skills or achievements obtained in the courses and (b) offer content personalization based on their preferences and characteristics identified automatically. In the context of software engineering, software architectures represent a key aspect of software development; therefore, research on the analysis, design, development, and evaluation of software architectures for AMLSs is fundamental [9; 10]. This fact has inspired several works [5; 6; 7; 8; 9; 10] to propose software architectures for developing AMLSs with different characteristics and functionalities. However, these works fail to fully consider the requirements for implementing adequate adaptive learning and all the requirements that arise from the system model characteristics of mobile distributed systems. This has led to several issues, such as a lack of standardization and low quality of the developed AMLS in terms of requirements accuracy and coverage. There is clearly a gap in the research that provides a comprehensive body of knowledge on the development of AMLS [9; 10]. In this context, some works [5; 6; 7; 8; 9] have conducted literature reviews and systematic mapping studies of AMLSs to generate a knowledge base of the implemented adaptive approaches, machine learning techniques used, adaptation types of the proposed systems, challenges, and trends in general. However, we note that no study has presented an SLR of these works to identify the key aspects to be considered in software architectures to develop quality AMLSs, making it difficult to assess the proposed software architectures' quality and identify trends, research gaps, and future dimensions. Therefore, this work presents a systematic literature review (SLR) of software architectures oriented to developing AMLS proposed in the state-of-the-art. The proposed SLR aims to compare and identify critical aspects of the software architectures proposed in the specialized literature for developing AMLS. For this SLR, the search was performed using the following sources: the ACM Digital Library, IEEE Xplore, Science Direct, Springer, and SCOPUS. Studies published in English that describe and evaluate a software architecture to develop AMLS were included. Twenty-two primary works were relevant to the present study.