Backgrounds and scope: Personalization in e-learning is to some extent the immanentfeature of this type of education as it by definition should enable obtaining new skills andknowledge at individually chosen time and place. It cannot be denied, however, thatsignificant change in efficiency of such processes can be achieved solely when not only thelearning conditions are adapted to one’s individual needs but, first of all, the forms andmethods of presenting learning content are applied and various types of activities andinteractions are implemented. To achieve such goals quite often sophisticated solutionsbased on the use of artificial intelligence techniques are applied like in ITS (IntelligentTutoring Systems) or in AHS (Adaptive Hypermedia Systems). Personal data that determinecreating personalized learners’ profiles used by such systems are usually demographicaldata like: age, gender or place of living or some sorts of activities performed by the learnerand registered by the tools built into e-learning platform. On the other hand, such solutionsusually do not take into account individual learning experience, learning habits and customs,which, in fact, determine the way we absorb new pieces of information and realize new tasks.One of the means that enable more accurate approach to personalization is based onlearning styles theory. It is a commonly held belief that recognition of students’ learning styles may significantly increase teaching results both in traditional and online teaching and learning.
COE research and results: COE SGH research works on personalization can be characterized as a joined approach based on cognitive psychology on the one hand and on sophisticated artificial intelligence solutions on the other hand, which makes elaborated solution to extend the limitations of typical system of ITS or AHS class. Application of AI techniques enables more precise and automatic tracking of learning processes. At the same time the learners profile stored in a system and based on recognition of one’s individual set of learning styles supplies the information already gathered, what, in consequence, supports better adjustment of automatically created personalized online courses. A KS-TIW questionnaire elaborated for that purpose is based on Howard’s Gardner Multiple Intelligences Theory. It allows to recognize 7 different learning styles and brings the information about the extent to what each of measured learning styles is used by every individual learner. Collected data can be then used as the indicators for creating an individually tailored chain of learning objects that constitute highly personalized online course.