## 4 Results
### Analysis of the Architectures of Identified Platforms
The main elements of the architecture have the following functionalities: Amazon Elastic Map Reduce to collect and analyze user learning events in real time, Amazon Simple Storage Service (S3) that handles data storage, and Amazon CloudFormation, whose purpose is to manage instances of other Amazon services for scalability. Adaptive content is displayed using an API interface that communicates with the Amazon RDS and Cassandra databases [85]. DreamBox is an intelligent adaptive learning system that exemplifies the adaptation of real-time content. Its core principle is that the learning content dynamically adjusts to the learner's responses and actions. Teachers can benefit from this system by accessing comprehensive records of student activity for analysis and insights. Figure 3: Knewton architecture (based on [85]). Figure 4 provides a visual representation of the DreamBox architecture, which is based on the official information found in [89; 90]. The foundation of an intelligent adaptive learning system consists of several key elements:  
* Modularized curriculum: this allows flexible content delivery that can be easily adapted to individual learners' needs;
* Continuous data collection: the system constantly gathers data on the student's progress and performance;
* Cognitive model: this model serves as the brain of the system, interpreting student data to personalize the learning experience [91]. The cognitive model plays a crucial role in achieving personalization and content adaptation [89]. Processes information about the learner and his activity, creating a real-time snapshot of his current understanding. This enables the system to determine the next steps in the learning process, set an appropriate pace, and tailor the curriculum and activities accordingly. A continuous data collection and processing loop is essential for the effectiveness of a cognitive model [92]. DreamBox focuses on mathematics and reading for pre-K-12 students, providing a targeted and adaptive learning experience in these core subjects. The architecture proposed by [93] relies on multiple interacting agents to facilitate personalized learning tailored to each student's needs. These agents consider various factors, including learning style, knowledge level, and visual or auditory impairments, to create personalized learning paths and suggest appropriate content. Figure 5 illustrates the architecture of the multi-agent system, which is based on the original design presented in [93]. Figure 4: DreamBox architecture (based on [89]). The key components of this architecture include the following:  
* Learner agent: this component stores information about each learner, such as their name, age, learning progress, style, and any disabilities;
* Content agent: This component houses the learning content, organized hierarchically from the course level to individual learning objects. The content is personalized according to the needs of each learner;
* Adaptation agent: This component is responsible for creating a personalized learning experience. It continuously interacts with the learner and content models, using the Q-learning algorithm to select appropriate learning objects on the basis of the learner's characteristics and needs [93]. The system is termed \"multi-agent\" because it consists of several agents that work together, each with specific roles, to provide learners with relevant content. Agents communicate and share information, contributing to the overall efficiency and effectiveness of the system.