Labeling the nodes in the intrinsic order graph with their weights

[EN] This paper deals with the study of some new properties of the intrinsic order graph. The intrinsic order graph is the natural graphical representation of a complex stochastic Boolean system (CSBS). A CSBS is a system depending on an arbitrarily large number n of mutually independent random Boolean variables. The intrinsic order graph displays its 2n vertices (associated to the CSBS) from top to bottom, in decreasing order of their occurrence probabilities. New relations between the intrinsic ordering and the Hamming weight (i.e., the number of 1-bits in a binary n-tuple) are derived. Further, the distribution of the weights of the 2n nodes in the intrinsic order graph is analyzed…

Complex sochastic boolean systems : new properties of the intrinsic order graph

[EN]A complex stochastic Boolean system (CSBS) is a system depending on an arbitrary number n of stochastic Boolean variables. The analysis of CSBSs is mainly based on the intrinsic order: a partial order relation defined on the set f0; 1gn of binary n-tuples. The usual graphical representation for a CSBS is the intrinsic order graph: the Hasse diagram of the intrinsic order. In this paper, some new properties of the intrinsic order graph are studied. Particularly, the set and the number of its edges, the degree and neighbors of each vertex, as well as typical properties, such as the symmetry and fractal structure of this graph, are analyzed…

Ecucational data mining (EDM) para la determinación de comportamientos en estudiantes de ingeniería en la modalidad virtual de UDE

[ES]En este artículo se describe la experiencia de la aplicación de técnicas de EDM (clustering) a un curso disponible en la plataforma Ude@ de la Universidad de Antioquia. El objetivo es clasificar los patrones de interacción de los estudiantes a partir de la información almacenada en la base de datos de la plataforma Moodle. Para ello, se generan informes sobre el uso de los recursos y la autoevaluación que permiten analizar el comportamiento y los patrones de navegación de los estudiantes durante el uso del LMS (Learning Management System).