M. C. Verri

Data mining models for student careers

We presents a data mining methodology to analyze the careers of University graduated students.We present different approaches based on clustering and sequential patterns techniques.We introduce the concept of ideal career.We compare the career of a generic student with the ideal one.We apply the methodology to a real case study and interpret the results. This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using the different techniques just introduced. Finally, we apply the methodology to a real case study and interpret the results which underline that the more students follow the order given by the ideal career the more they get good performance in terms of graduation time and final grade.

Waiting patterns for a printer

We introduce a model based on some combinatorial objects, which we call l-histograms, to study the behaviour of devices like printers and use the combinatorial properties of these objects to study some important distributions such as the waiting time for a job and the length of the device queue. This study is based on an important relation between 1-histograms, generating trees and binary trees.

Combinatorial sums and implicit Riordan arrays

In this paper we present the theory of implicit Riordan arrays, that is, Riordan arrays which require the application of the Lagrange Inversion Formula to be dealt with. We show several examples in which our approach gives explicit results, both in finding closed expressions for sums and, especially, in solving classes of combinatorial sum inversions.

An Algebra for Proper Generating Trees

We find an algebraic structure for a subclass of generating trees by introducing the concept of marked generating trees. In these kind of trees, labels can be marked or non marked and the count relative to a certain label at a certain level is given by the difference between the number of non marked and marked labels. The algebraic structure corresponds to a non commutative group with respect to a product operation between two generating trees. Hence we define the identity generating tree and the inverse of a given generating tree.

Finding Regularities in Courses Evaluation with K-means Clustering

This paper presents an analysis about the courses evaluation made by university students together with their results in the corresponding exams. The analysis concerns students and courses of a Computer Science program of an Italian University from 2001/2002 to 2007/2008 academic years. Before the end of each course, students evaluate different aspects of the course, such as the organization and the teaching. Evaluation data and the results obtained by students in terms of grades and delays with which they take their exams can be collected and reorganized in an appropriate way. Then we can use clustering techniques to analyze these data thus show possible correlation between the evaluation of a course and the corresponding average results as well as regularities among groups of courses over the years. The results of this type of analysis can possibly suggest improvements in the teaching organization.

Combinatorial inversions and implicit Riordan arrays

Abstract In this paper we present the theory of implicit Riordan arrays, that is, Riordan arrays which require the application of the Lagrange Inversion Formula to be dealt with. We show several examples in which our approach gives explicit results in solving classes of combinatorial sum inversions.

University Student Progressions and First Year Behaviour.

Advanced mining techniques are used on educational data concerning university students. In particular, cluster analysis is used to predict the university careers of students starting from their first year performance and the results of the self assessment test. The analysis of the entire careers highlights three groups of students strongly affected by the results of the first year: high achieving students who start medium-high and increase their performance over the time, medium achieving students who maintain their performance throughout the entire course of study, low achieving students unable to improve their performance who often abandon their studies. This kind of knowledge can have practical implications on the involved laurea degree.

AN ANALYSIS OF A SIMPLE ALGORITHM FOR RANDOM DERANGEMENTS

We consider the uniform generation of random derangements, i.e., permutations without any fixed point. By using a rejection algorithm, we improve the straight-forward method of generating a random permutation until a derangement is obtained. This and our procedure are both linear with respect to the number of calls to the random generator, but we obtain an improvement of more than 36%. By using probability generating functions we perform an exact average analysis of the algorithm, showing that our approach is rather general and can be used to analyze random generation procedures based on the same rejection technique. Moreover, emphasis is given to combinatorial sums and a new interpretation of a known infinite lower triangular array is found.

Average-Case Analysis for a Simple Compression Algorithm

Abstract. In this paper we treat the static dictionary problem , very well known in computer science. It consists in storing a set S of m elements in the range [1 . . . n ] so that membership queries on S s elements can be handled in O(1) time. It can be approached as a table compression problem in which a size n table has m ones and the other elements are zeros. We focus our attention on sparse case (m

The Influence of First Year Behaviour in the Progressions of University Students

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