# A Modular and Semantic Approach to Personalised Adaptive Learning: WASPEC 2.0
## 6 Discussion and Related Work  
There have been several attempts in the literature to create educational systems to enable personalised and adaptable learning, with researchers taking various techniques, as stated in previous sections. The approach employed in the creation of the WASPEC platform is unique in that various tools commonly used in educational systems are combined. According to a review of current trends in personalised adaptive educational systems by [PERSON] [20], modular frameworks, semantic web technologies, and standardisation are some of the most frequent technologies utilised to facilitate interoperability and scalability in the development of such systems. Modular frameworks are integrated into the WASPEC platform through the usage of Moodle LMS and pedagogical agents. Modular frameworks \"permit individuality, flexibility, exchangeability, and the extension of a system\", and \"they are seen as an effective solution for shortcomings found in complete learning designs\" ([21], as cited in [20]). In the literature on e-learning systems, the semantic web has become an increasingly attractive research area. The technologies are divided into two sub-themes: ontologies and query language. Semantic web technologies enable the formalisation and sharing of knowledge  
Figure 14: Ontologies and Rules in WASPEC 2.0.  
across several platforms. EOMPP and SWRL rules that describe personalisation choices and relationships between different elements on the platform are used to incorporate semantic web technologies. Finally, standardisation aims to create \"a set of widely adopted and interchangeable features, to make instructional contexts compatible with each other, interoperable, and repeatable\" [20]. The LOM standard elements essentially specify how the domain is configured on the WASPEC platform. Some platforms in the literature are designed exclusively with a multi-agent system network using modular frameworks. MASHA-EL (Multi-Agent System Handling Adaptivity for E-learning), in which learners use a variety of devices, is assisted by a device and teaching agent [22], a ubiquitous teaching assistant (u-TA), capable of assisting students in problem-solving activities during laboratory sessions [23], and AILS (Adaptive Intelligent Learning System), developed with JADE (Java Agent Development Framework), which provides content, presentation, participation, and perspective adaptation [24], are examples of such designs. Personalisation via extending LMSs includes designs such as MAL (Moodle Adaptive Learning), which allows learners to be customised based on their learning styles [25]. MAL was developed as an ontology to define the connections between learners, learning resources, and learner actions on the platform. SWRL rules were also developed to identify adaptive functions during learning. [PERSON] et al. [26]'s AAST (Arab Academy for Science and Technology and Maritime Transport) Moodle implementation presents a personalisation model based on the Felder-Silverman Learning Style model and the Myers-Briggs personality type indicator. Personalized Learning Management System (PLeMSys), a design by [PERSON] et al. [27], allows learners to obtain customised learning experiences on Moodle via plugins based on their degree of competence and learning style. This implementation specifies two levels of personalisation: the first is based on the student's knowledge level and the second on their learning methods. There is the popular Protus (PROgramming TUtoring System) implementation with semantic web technologies, a Java programming language teaching system that recommends effective learning resources to learners depending on their learning preferences [28]. TANGRAM, an intelligent information system, is yet another ontology-based design. Learning resources are customised to the learner's current knowledge level, learning style, and personal preferences [29].