# A Modular and Semantic Approach to Personalised Adaptive Learning: WASPEC 2.0
## 5 The Architecture of WASPEC 2.0  
As previously stated, integrating personalisation components into an existing open-source learning management system is an efficient approach of developing personalised adaptive learning systems. These systems frequently do not offer personalisation by default. However, despite the lack of features for customisation, they have very sophisticated administrative and learning functionalities that would be pretty difficult to recreate if a design were designed from the ground up. As a result, the WASPEC design supports modular components that can communicate with any LMS that supports web services. The Service Framework, Semantic Framework, and Multi-Agent System described in Section 2 are among these components that comprise the personalisation engine. Laravel was used to implement the Service Framework, which provides the personalisation of learning resources in the background of Moodle Learning Platform.  
One of the comments made by participants in a prior review of the WASPEC platform [4] by researchers and instructors knowledgeable with personalised learning was that the process of personalisation on the platform might be too complex for a non-technical individual to work with. To overcome this issue, the next implementation and testing iteration recommends implementing the personalisation components as a plugin that can be deployed on Moodle. Moodle was chosen because it is a popular and widely used LMS with a variety of features that was designed with both pedagogy and technology in mind. Personalisation can be done directly on the Moodle platform using this architecture, rather than switching from one platform to another during the process. Because the architecture (Figure 13) is very modular, the personalisation components can be implemented with minor adjustments on a different LMS, such as ILIAS [17] (which was originally intended for this project).  
The architecture incorporates similar components that were part of the initial WASPEC implementation's framework [4]. The primary distinction is that the SF is no longer included in this design. The learner model stores critical information about a learner on  
Figure 13: The Architecture of WASPEC 2.0.  
the learning platform at all times. The domain describes the relationship between courses, competencies, and LOs. The pedagogical model specifies guidelines that enable rule-based reasoning for personalisation and adaptivity of learning content.  
EOMPP describes a slightly more improved version of the set of ontologies used for the design in the first implementation [4]. It describes the links between the different models: domain, learner, and pedagogical. The element representation in EOMPP enables knowledge modelling, semantic annotation of learning resources, and semantic reasoning. EOMPP is made up of various ontologies, as illustrated in Figure 14.  
The learner and domain ontologies are created by using the D2 RQ platform [18] to map the learner and domain repositories as RDF schemas. The learner model ontology holds generic and adaptive learner information. The domain ontology is defined as a taxonomy of concepts and competencies, with features and interconnections that connect them to other concepts in a hierarchical order. The link between personalisation parameters and metadata components is described by the task ontology. It also illustrates how user features connect to domain model elements. Finally, there is the pedagogical ontology, which provides personalisation possibilities, as well as guidelines to follow when making adaptive judgments.  
The agents in the MAS are responsible for tracking learner progress, updating learner profiles, providing adaptivity, and updating the domain. These updates are made possible in EOMPP by OWLready2 [19], a Python module that provides access to OWL ontologies.