# Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review
## 2 Related Work  
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. These related works are described below. [PERSON] and [PERSON] [5] compared existing learning models in adaptive mobile learning (m-learning) systems. Their main objective was to identify how these models consider the characteristics of the student and their context to provide educational content adapted to each individual. The study examined various solutions in adaptive learning systems, focusing on how learner characteristics (expectations, motivation, learning styles,habits, and needs) combine with the parameters of the learner's environment to establish an adequate learning context. Key findings of the study revealed that none of the existing systems fully integrated key learner characteristics with environmental parameters effectively to establish a complete learner context. A significant challenge identified for developers was determining the learner's important characteristics that significantly influence the learning process and its performance. For their part, [PERSON] et al. [6] conducted a systematic review to analyze the contextual analysis approach in mobile (m-learning) and ubiquitous (u-learning) learning processes. The objective was to identify the variables used in the last decade for contextual analysis and how these are applied in said learning processes. Context modeling in mobile and ubiquitous learning analysis is based on the interpretation of three classes of variables. Internal variables include aspects such as learning styles in the educational context, while external variables cover the information recorded on mobile devices. Activities refer to connection to services and user interfaces. In recent research, considerable attention has been paid to the use of hardware variables, such as battery sensors and network connection, which are crucial for the efficient delivery of content to students. For the future, the authors propose the creation of an omnichannel architecture for educational environments in m-learning, a concept that holds immense potential for revolutionizing the way we deliver education through mobile devices. This architecture should consider academic, social, technological, and environmental variables. [PERSON] and [PERSON] [7] presented an SLR to understand better the development of context-aware mobile learning applications (CAMLAs). These applications offer educational resources tailored to students' specific needs and circumstances. The study identified eight types of contexts used to develop CAMLAs. These include physical condition, time, learner, cognitive, learning style, location, device, and people, with learner, location, and time contexts being the most common. Contextual information is collected using virtual and physical sensors and is mainly represented as relational or ontological data. The context detection layer captures these data, while the data layer is responsible for its storage and management. Six adaptation mechanisms were identified in CAMLAs: adaptation of learning resources, presentation of resources, feedback and support, navigation to locations, communication and interaction, and hardware resources. CAMLAs were classified into four categories: general adaptation, feedback and support, navigation to locations, and communication and interaction. [PERSON] et al. [8] conducted a systematic review to analyze the recent progress in the research and development of Personalized and Adaptive Context-Aware Mobile Learning (PACAML). The review identified the contextual information used in PACAML studies, highlighting the importance of adapting learning according to the student's context.