Analysis of students' study paths using finite Markov chains

Student's studies can be seen as consisting of chains or sequences of courses, the learning activities of a student along these lines make his study path. In this paper, methods for detection, analysis and control of learning activities are developed, based on students' credit record data. A central aim of the work is to support the supervision of students using currently available data bases.