Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems

In designing intelligent web based educational systems different student needs and preferences should be taken into consideration. Personalization of a system usually results in an increase in its effectiveness, which can be measured by the degree to which learning outcomes are achieved. However, taking into account the individual requirements of each learner and adjusting the system to their needs may be very costly in cases of large numbers of students. Finding groups of learners with similar preferences seems to be a good solution which allows for differentiating the system in compliance with the needs of group members. Learner preferences depend on characteristic traits such as cognitive features, including dominant learning styles. Students modeled by AbstrAct