Personalizing learning materials for students with multiple disabilities in virtual learning environments

Current efforts towards including students with disabilities in web-based higher education are well established. However, existing learning environments are not fully inclusive, particularly for those with multiple disabilities. Most learning environments built for students with disabilities limit themselves to meeting the needs of specific disabilities and do not attempt to scale up to the difficulties of designing for those with multiple disabilities. This paper seeks to address how virtual learning environments (VLEs) can be designed to include the needs of learners with multiple disabilities. Specifically, it employs AI to show how specific learning materials from a huge repository of learning materials can be recommended to learners with various disabilities. This is made possible through employing semantic web technology to model the learner and their needs. Three techniques are discussed to combine requirements. Simple logical operators, knowledge based rules, and machine learning based rule induction are combined in this integrated approach. It is hoped that developers of e-learning systems will be encouraged from this approach to design fully inclusive virtual learning environments.