Data mining for a student database

Educational data mining is an emerging research area that produces useful, previously unknown issues from educational database for better understanding and improving the performance and assessment of the student learning process. This paper presents some data mining models to analyze the careers of University students, introducing a new approach to this research area. The career of a student can be analyzed from various points of view, among which the following two are particularly important: i) the perspective of the student, who evaluates how difficult and important an exam is, in order to decide to take it immediately at the end of the course, or delay it as much as possible; this aspect is studied in Section 2 with cluster and classification algorithms by introducing a notion of distance between careers; and ii) the perspective of each course, by analyzing the distribution of students with respect to the delay with which they take an examination, to discover common characteristics between two or more courses; this is done in Section 3 in terms of Poisson distributions.