Abstract
One of the most hard and expensive tasks commonly attributed to e-learning is to create fresh new content. Generally, educators try to reuse pre-existent material which suffers some modification before being given to students. A repository of such educational material is therefore of great use. In this article we propose a digital repository in which content ingestion includes an unsupervised classification mechanism which organizes documents into clusters of semantic proximity. This mechanism can be afterwards enhanced with a social classification process based on tagging. We present a methodology for the evaluation of our proposal with and without using tags. © 2011 IADIS.

Abstract
We present a novel dynamic graphical representation of the interactions between students and teachers in online forums available in Moodle. By defining the relationships between the users as a graph, it is possible to apply techniques of social network analysis. This system brings up new possibilities to e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation and to find the implicit relations between forums participants. © 2011 IADIS.

Abstract
Many natural and artificial structures can be represented as complex networks. Computing the frequency of all subgraphs of a certain size can give a very comprehensive structural characterization of these networks. This is known as the subgraph census problem, and it is also important as an intermediate step in the computation of other features of the network, such as network motifs. The subgraph census problem is computationally hard and most associated algorithms for it are sequential. Here we present several increasingly efficient parallel strategies for, culminating in a scalable and adaptive parallel algorithm. We applied our strategies to a representative set of biological networks and achieved almost linear speedups up to 128 processors, paving the way for making it possible to compute the census for bigger networks and larger subgraph sizes.

Abstract
Complex networks are ubiquitous in real-world and represent a multitude of natural and artificial systems. Some of these networks are inherently dynamic and their structure changes over time, but only recently has the research community been trying to better characterize them. In this paper we propose a novel general methodology to characterize time evolving networks, analyzing the dynamics of their structure by labeling the nodes and tracking how these labels evolve. Node labeling is formulated as a clustering task that assigns a classification to each node according to its local properties. Association rule mining is then applied to sequences of nodes' labels to extract useful rules that best describe changes in the network. We evaluate our method using two different networks, a real-world network of the world annual trades and a synthetic scale-free network, in order to uncover evolution patterns. The results show that our approach is valid and gives insights into the dynamics of the network. As an example, the derived rules for the scale-free network capture the properties of preferential node attachment.

Abstract
Learning management systems are routinely used for presenting, solving and grading exercises with large classes. However, teachers are constrained to use questions with pre-defined answers, such as multiple-choice, to automatically correct the exercises of their students. Complex exercises cannot be evaluated automatically by the LMS and require the coordination of a set of heterogeneous systems. For instance, programming exercises require a specialized exercise resolution environment and automatic evaluation features, each provided by a different type of system. In this paper, the authors discuss an approach for the coordination of a network of eLearning systems supporting the resolution of exercises. The proposed approach is based on a pivot component embedded in the LMS and has two main roles: (1) provide an exercise resolution environment, and (2) coordinate communication between the LMS and other systems, exposing their functions as web services. The integration of the pivot component in the LMS relies on Learning Tools Interoperability (LTI). This paper presents an architecture to coordinate a network of eLearning systems and validate the proposed approach by creating such a network integrated with LMS from two different vendors.

Abstract
Web interfaces are used nowadays for virtually every kind of computer application. The proliferation of web interfaces created the need to collect and analyze data on how users interact with them. Many web applications used for this purpose rely on what can be called a meta web interface. Meta web interfaces are used for different purposes but they share a set of common features: a web interface based on the subject interface with a second layer interface for collecting data, a central repository for persisting the collected data, and an API for retrieving aggregated data on user interaction. This paper describes Z-Web - a framework for developing meta web interfaces that provides these three features. To create a second layer in the meta web interface a Z-Web server is placed as a proxy between the web client and the subject web server and injects modifications while forwarding HTTP requests. These modifications are typically JavaScript libraries that collect and store data related to user interaction. The framework caches the pages it proxies and provides persistent storage for the collected data. An application interface (API) makes this data available to client application supported by Z-Web. This paper presents an overview of Z-Web, with the general architecture of a web application based on this framework, and describes the design and implementation issues of its main components. Two systems developed with Z-Web are also presented to evaluate the applicability of the framework and its overhead when compared with similar systems.