FML-based intelligent adaptive assessment platform for learning materials recommendation

There are many students learning their academic studies via on-line education platform with many learning materials; however, how to select learning materials that exactly fit to their competence is not easy for them. This paper proposes an intelligent adaptive assessment platform (IAAP) to allow students to do adaptive testing to assess their learning ability. Additionally, this paper also proposes an FML-based fuzzy inference mechanism to infer the rank of the recommended learning materials based on the constructed fuzzy ontology, including knowledge base and rule base. The learning-material recommendation mechanism then outputs the recommended learning materials to the students based on the constructed ontologies and the inferred rank of the recommended learning materials. Finally, after learning, students provide a feedback to the IAAP and starts next learning iteration to achieve the goal of students´ learning progress. Experimental results show that the developed IAAP is able to correctly estimate students´ ability and the proposed approach is feasible for learning materials´ recommendation and self-learning.