# A Modular and Semantic Approach to Personalised Adaptive Learning: WASPEC 2.0
## 1 Introduction  
Initially, e-learning systems were solely intended to provide students with material asynchronously; however, with the emergence of personal computers and the internet, they evolved into more interactive systems. Institutions such as the Open University, which largely focused on distance learning, took advantage of the Internet's capabilities to provide course material by e-mail rather than post or mail [1]. E-learning platforms of today are effective at tracking learning activities and managing learning processes, both asynchronously and synchronously. With the growing number of organisations and institutions utilising these systems, one important shortcoming is the inability to accommodate diversity in learner needs and preferences, which may influence learning. Personalised adaptive learning is a concept that seeks to address that. Personalised adaptive learning is not a new notion; it is the outcome of integrating the goals of personalised learning with the pedagogical and technological opportunities that new ICT (Information and Communications Technology) tools bring. Personalised adaptive learning is a combination of technologies that support Intelligent Tutoring Systems (ITS) and Adaptive Hypermedia Systems (AHS), a group of systems referred to by [PERSON] and [PERSON] [2] as Adaptive and Intelligent Web-based Educational Systems. The major goal of these systems is to support personalised and adaptive learning by utilising domain information, learner knowledge, and teaching methodologies. AHS leverage information from the learner model to give personalised instruction specific to each learner, whereas ITS provide the same level of personalisation for all learners by incorporating artificial intelligence technology [2, 3]. There are two broad techniques that academics and educators typically use to build and implement the architecture of personalised learning. The first approach would be to design the educational system from the ground up, with all of its various components and rules for personalisation. The second strategy entails complementing existing educational systems that do not provide default personalisation options. The first approach has some drawbacks, including the difficulties of sharing personalisation logic and content owing to compatibility issues, as well as inflexibility in terms of extensibility and application to multiple domains. This is due to the fact that these systems deliver personalisation in a specific domain, and the personalisation strategies are built in the learning content or instruction. The learner model is also largely dependent on how the system's adaptive engine is developed. AHS, for example, uses various learner models to tailor the content and links of educational hypermedia spaces to the learners [2]. Even if the different models in the architecture are separated, the personalisation strategies are frequently interwoven in the domain model, learner model, or the system's business logic. As a result, the business logic for personalisation is so entangled with the system's basic operations that modification is impossible without interfering with other components unrelated to personalisation. To use various pedagogical models or personalisation strategies in a different domain, the learner model, content model, and system's business logic would typically need to be re-authored. The second approach suggests a modular design in which personalisation is provided to e-learning systems as an additional functionality. The business logic for personalisation is embedded in the architecture of a domain-independent system using this method. The engine that supports personalisation is completely independent from the essential functionalities of an e-learning system, such as storing learning content and basic learner information. This modular method is used in the design of WASPEC, a Weighted Agent System for Personalised Curriculum [4], which is implemented by extending Moodle [5].