The rapid development and popularity of Internet and mobile technologies has led researchers to focus on the usability of technology in education, both in formal education and in open and distance education. Learning systems are examples of technologies that help tutors and learners in sharing course information, delivering course materials, applying quizzes. In a learning system, it is very important to consider individual characteristics in order to attract the attention of the student and increase the level of engagement. Adaptive learning systems provide students an individual learning environment by assessing students' skills and demographic differences, tailoring the learning environment to these differences in skills and characteristics. Adaptive learning systems evaluate the user data and incorporate the characteristics of students or users in a diverse population of learners into a user model. The most important feature of any adaptive learning system is the user model, which represents information about each user. The user model is used for decisions which individualize the system. The success of the system in decision-making depends on the user model and the success of the user model depends largely on the methods of collecting and evaluating user data. In the present data age, when it is possible to collect all kinds of data about the user from different sources; examining these data and its properties is critical. The paper overviews the existing studies on user modeling in adaptive learning systems and examines the data types used in the studies and the user characteristics that form the basis for the user models.
Adaptive learning systems, user model, learner model, user data, user characteristics.
| Author : | Neslihan ADEMİ -Suzana LOSHKOVSKA |
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| Number of pages: | 507-518 |
| DOI: | http://dx.doi.org/10.29228/TurkishStudies.24822 |
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