The study examines the sensation seeking tendency – Zuckerman’s concept (1978) and student’s learning style – Dunn’s concept (2000). The study is of descriptive character and uses quantitative methods. To collect data, two standardized questionnaires are combined into one – Learning styles, Interests and Hobbies Inventory. Students of Czech grammar schools with extended physical education and sports training were observed. Analysis of Variance has found that boys seek sensation extensively more than girls regardless of their class type; furthermore, students from classes with P.E. and sports specialization do not seek sensation more extensively in comparison with students from general classes. Statistically significant differences in preferred learning styles have been identified between both types of classes.

The paper deals with learning styles and their initial diagnostics in the process of the student’s learning. It is focused on a method of learning styles recognition with the support of modern information technologies. The paper analyses different methods of the learning styles diagnostics, incorporating this issue into the scientific field of artificial intelligence and presents an idea on how to diagnose a learning style by using an unconventional fuzzy logic linguistic expert system. The expert system was designed to diagnose learning styles of university students in adaptive computer aided learning systems. A significant benefit is continuous numerical evaluation of the student’s degree of affiliation to all learning categories (types of student) with a possibility of simple determination of dominant and subdominant types, the use of a linguistic rule-based decision-making model, which is completely transparent and open, and the use of a decision-making procedure corresponding to the process of human consideration. The paper is an example of an application of modern information technologies in education.

This paper examines the styles and approaches to learning in contemporary higher education students. These individual characteristics are seen as results of the interaction between student individuality and the learning environment stimuli. The presented research is based on the assumption of existing interactions among the nature of study environment, the student’s approach to learning and his/her study effectiveness. Research results confirm this assumption and enable to analyze findings in the context of a specific learning environment.

User interface becomes the major channel to convey information in e-learning context: a well-designed and friendly interface is thus the key element in helping users to get the best results quickly. This paper investigates the importance of a certain choice offered: if several graphical user interface designs are offered to distance learning students of known learning styles, should we find any preferences? To find that, a procedure for determining association between learning styles and GUI was devised. A total of 51 participants were tested to find out if there was any correlation between students’ learning styles and their GUI preferences. We have found that the fact of having any preference towards a GUI is associated with AC score of Kolb’s model.

E-learning instruction adapted according to student study characteristics stands on three basic pillars: a definition and recognition of the student’s learning style, adaptable study supports and a correct assignment of an instructional style to a specific student and his/her learning style. This paper is a continuance of the paper [1] about student learning style analysis and adds the “second pillar”, the study support analysis and the design of their structure in a way which would enable a compilation of study supports optimized and tailored to all the student types with their different learning styles.