# A Modular and Semantic Approach to Personalised Adaptive Learning: WASPEC 2.0
## 3 The Architecture of WASPEC  
Figure 3 depicts the WASPEC architecture, which consists of the following primary components: an Learning Management System (LMS), a Service Framework (SF), a Semantic Framework (SmF), and a Multi-Agent system (MAS). The platform is accessed by the learners via the LMS, which is Moodle for this implementation. The learners' interactions with the platform, as well as exercises and questionnaires, are used to determine their preferences and needs. This data is saved in the learner model. Personalisation is implemented in the SF (designed with Laravel). On the SF, the course instructor specifies personalisation criteria and metadata specification items. These are used for semantic annotation of existing courses and content on the LMS. Figure 2: The Framework of Personalised Adaptive Learning. Web services are used by the SF to communicate with the LMS. When application programs need to communicate with one another, web services provide effective interoperability solutions. A service, in this sense, is a remote function that is independent of the implementation and can be run when called [13]. The usage of web services offers the flexibility to \"only call the services when needed, allowing for efficient integration of personalisation technologies\" [4]. The web services enable numerous LMS functions, including semantic annotation of LMS courses, educational materials, and learners. The SF and LMS data repositories are specified as graph databases in the SmF and represented in EOMPP (E-learning Ontology for Multi-Parameter Personalisation), allowing the selection and combining of relevant personalisation criteria for each course [4]. The classes of EOMPP, designed for the implementation of the WASPEC platform, are based on the elements of the IEEE Learning Object Metadata (LOM) standard [14]. The LOM standard was chosen for it's ease of use, extensiblity, and popularity in e-learning applications. Rules set in the semantic framework and pedagogical agents in the MAS dynamically update the learner profile with data about each student's learning state and behavior. The graph databases are then joined together to build semantic rules for personalisation and adaptivity. Web services are used to communicate to the LMS the personalisation of learning content and the reconfiguration of learner profiles. Personalisation on the WASPEC platform is done in three stages and with a multi-parameter approach. The phases determine what learners have to learn, how they wish to learn, and how well they have learned. The first phase is based on their level of knowledge (personal needs), the second on other preferences based on individual features, and the third on their performance and learning behaviour (personal development and individual characteristics). Two indicators (LOR-PD and CRLO-PD) were designed to choose and combine personalisation parameters in order to enable multiple-parameter personalisation in a scalable  
Figure 3: The Architecture of WASPEC. manner [15]. A personalisation strategy for a specific learning scenario is defined by the selection and combination of personalisation parameters. A personalisation strategy decides whether or not a learning object (the smallest unit of learning) is personalised. Figure 4 depicts the selection of various parameters to generate various learning strategies. The LOR-PD (Learning Object Representation based on Dimensions of a Personalisation parameter) and CRLO-PD (Complementary Ratio of Learning Objects based on Personalisation parameter Dimensions for each competency) indices for a personalisation parameter demonstrate how a personalisation parameter is represented in a course depending on included dimensions. While the LOR-PD is important for identifying suitable personalisation parameters, as illustrated in Figure 4, the CRLO-PD defines which dimensions of a specified personalisation parameter are used for customisation of each learning object.