Design and Development of a Fuzzy Learners’ Knowledge Modelling System - Ng, Wen Thing (Universiti Malaysia Sarawak (UNIMAS), 2019)
Various past studies reported on how to personalise learning via intelligent systems, but few studies reported on how to assist instructors in providing personalised learning via leverage of technologies. Instructors are knowledgeable in teaching and learning but they shy away from personalising learning due to its complexity. This study thus aims to design and develop a learners’ knowledge modelling system that is able to assist the instructors in personalising learning. This study focuses on three issues related to the complexity of personalising learning. First issue concerns about how the proposed system fit with the desired curriculum. Another two issues are about reasoning and examining the knowledge of a large group of learners. Various relevant past studies are discussed in this study, in order to identify and justify the available technologies for tackling these issues. A key to this study is determining learners’ knowledge because it is lexically imprecise. This study attempts to embed a fuzzy inference system into the proposed system for reasoning learners’ knowledge, as it can mathematically deal with uncertainties. Details of the design and development of both proposed systems are described and justified in this study. This study also demonstrates the applicability of the proposed system via a real-world case study of an undergraduate course. In conclusion, the proposed system shows that it is able to inform the appropriateness of the instructor’s practice and assist them to plan for interventions to personalise learning. Future studies are needed to extend this study in terms of usability, scalability, sustainability and accessibility, in order to support large-scale implementation in future.