State-of-the-Art Ontology Annotation for Personalised Teaching and Learning and Prospects for Smart Learning Recommender Based on Multiple Intelligence and Fuzzy Ontology

With evolving technologies, a way of learning is broaden with availability of open teaching and learning materials. Educators and learners often find relevant resources that can assist them in their respective teaching and learning activities; however, most of the time they are bombarded with redundant and/or low-quality information sources via search engines. In the context of personalised teaching and learning, this becomes even more challenging as the educators and learners are looking for specific resources, for specific concepts, and for specific domain of interest. To add to this complexity, the emergence of Big Data is making the situation even worse as new information is emerging on the web every day; hence, the need for a system assisting in this is becoming more prominent. In this paper, we propose an efficient method to identify suitable teaching and learning resources in order to promote individual learning process. As the text or web data for presenting teaching and learning preferences for educators and learners is imprecise, inconsistent, and non-consensual, the smart learning system has potential to be developed based on fuzzy ontology and fuzzy recommendation engine which can address the uncertain and subjective judgments in determining teaching and learning material and evaluating multiple intelligence. We present research direction towards smart learning using fuzzy systems.