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This work is devoted to the development of personalized training systems. A major problem in learning environments is applying the same approach to all students: i.e., teaching materials, time for their mastering, and a training program that is designed in the same way for everyone. Although, each student is individual, has his own skills, ability to assimilate the material, his preferences and other. Recently, recommendation systems, of which the system of personalized learning is a part, have become widespread in the learning environments. On the one hand, this shift is due to mathematical approaches, such as machine learning and data mining, that are used in such systems while, on the other hand, the requirements of technological standards "validated" by the World Wide Web Consortium (W3C). According to this symbiosis of mathematical methods and advanced technologies, it is possible to implement a system that has several advantages: identifying current skill levels, building individual learning trajectories, tracking progress, and recommending relevant learning material. The analysis of feedback, academic advising, and recommendation systems underlies the proposed idea. The conducted research demonstrates how to make learning environments more adaptive to the users according to their knowledge base, behavior, preferences, and abilities. In this research, a model of a learning ecosystem based on the knowledge and skills annotations is presented. This model is a general model of the lifelong learning process. Second, this thesis focuses on the creation of tools for personalized assessment, recommendation, and advising. Third, it is concentrated on developing an adaptive learning game for children, which takes into account the differing perception of words by students during training. |
