Álvaro Figueira

Depicting online interactions in learning communities

In this article, we detail a system that provides contributes for analyzing and characterizing interactions that occur between participants of online communities. We adapted and applied the Social Network Analysis methodology to online discussion forums to create a dynamical interaction graph. The graph can be embedded in learning managements systems and accessed through a web page. The functionality of the system provides a suitable environment to characterize the interactions between actors and their participations in discussion forums. In the article we describe the use of the system in two real-world situations. Our conclusions lead to the verification and the rapid identification of some important situations that occur in learning communities, such as: the location of actors more or less active; distinction of positions and roles; identification of different ways of organization/interaction in groups; characterization of the interactions of a group or of a community as a whole.

Web-Based Intelligent Tutoring Systems Using the SCORM 2004 Specification - A Conceptual Framework for Implementing SCORM Compliant Intelligent Web-Based Learning Environments

This paper describes a conceptual framework for implementing Intelligent Tutoring Systems using SCORM 2004. The main objective is to discuss how the SCORM 2004 sequencing and navigation specification can allow the development of Intelligent Web-Based Learning Environments using the sequencing and navigation tracking data, and rule set. Our main argument is that SCORM 2004 sequencing and navigation specification can be used to implement the two main functionalities of an ITS, (1) the inner loop and (2) the outer loop.

A concurrent programming environment with support for distributed computations and code mobility

We propose a programming model for distributed concurrent systems with mobile objects in the context of a process calculus. Code mobility is induced by lexical scoping on names. Objects and messages migrate towards the site where their prefixes are lexically bound. Class definitions, on the other hand, are downloaded from the site where they are defined, and are instantiated locally upon arrival. We provide several programming examples to demonstrate the expressiveness of the model. Finally, based on this model we describe an architecture for a run-time system supporting concurrent, distributed computations and code mobility.

An Approach to Relevancy Detection: Contributions to the Automatic Detection of Relevance in Social Networks

In this paper we analyze the information propagated through three social networks. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors. In this paper we focus on the search for automatic methods for assessing the relevance of a given set of posts. We first retrieved from social networks, posts related to trending topics. Then, we categorize them as being news or as being conversational messages, and assessed their credibility. From the gained insights we used features to automatically assess whether a post is news or chat, and to level its credibility. Based on these two experiments we built an automatic classifier. The results from assessing our classifier, which categorizes posts as being relevant or not, lead to a high balanced accuracy, with the potential to be further enhanced.

Predicting Results from Interaction Patterns During Online Group Work

Group work is an essential activity during both graduate and undergraduate formation. Although there is a vast theoretical literature and numerous case studies about group work, we haven’t yet seen much development concerning the assessment of individual group participants. The problem relies on the difficulty to have the perception of each student’s contribution towards the whole work. We propose and describe a novel tool to manage and assess individual group. Using the collected interactions from the tool usage we create a model for predicting ill-conditioned interactions which generate alerts. We also describe a functionality to predict the final activity grading, based on the interaction patterns and on an automatic classification of these interactions.

Using the overlapping community structure of a network of tags to improve text clustering

Breadcrumbs is a folksonomy of news clips, where users can aggregate fragments of text taken from online news. Besides the textual content, each news clip contains a set of metadata fields associated with it. User-defined tags are one of the most important of those information fields. Based on a small data set of news clips, we build a network of co-occurrence of tags in news clips, and use it to improve text clustering. We do this by defining a weighted cosine similarity proximity measure that takes into account both the clip vectors and the tag vectors. The tag weight is computed using the related tags that are present in the discovered community. We then use the resulting vectors together with the new distance metric, which allows us to identify socially biased document clusters. Our study indicates that using the structural features of the network of tags leads to a positive impact in the clustering process.

EduBridge Social - Bridging Social Networks and Learning Management Systems

The exponential growth of social media usage and the integration of digital natives in Higher Education Institutions (HEI) have been posing new challenges to both traditional and technology-mediated learning environments. Nowadays social media plays an important, if not central, role in society, for professional and personal purposes. However, it’s important to highlight that in the mind of a digital native, social media is not just a tool, it is a place that is as real and as natural as any real-life world place where formal/informal social interactions happen. Still, formal higher education contexts are still mostly imprisoned in locked up institutional Learning Management Systems (LMS), while a new world of social connections grows and develops itself outside schools. One of the main reasons we believe to be persisting in the origin of the matter is the absence of a suitable management, monitoring and analysis tools to legitimize and to efficiently manage the relationship with students in social networks. In this paper we discuss the growing relevance of the “Social Student Relationship Management” concept and introduce the EduBridge Social system, which aims at connecting the most commonly used LMS, Moodle, and the most popular social network, Facebook.

Visualization of sentiment spread on social networked content: Learning analytics for integrated learning environments

Social Media has been disrupting traditional technology mediated learning, providing students and educators with unsupervised and informal tools and spaces where authentic learning occurs. Still, the traditional LMS persists as the core element in this context, while lacking additional management, monitoring and analysis tools to handle informal learning and content. In this paper, we present an integrated methodology that combines social network analytics, sentiment analysis and topic categorization to perform social content visualizations and analysis aimed at integrated learning environments. Results provide insights on networked content dimension, type of structure, degree of popularity and degree of controversy, as well as on their educational and functional potential in the field of learning analytics.

Analyzing Social Media Discourse - An Approach using Semi-supervised Learning

The ability to handle large amounts of unstructured information, to optimize strategic business opportunities, and to identify fundamental lessons among competitors through benchmarking, are essential skills of every business sector. Currently, there are dozens of social media analytics’ applications aiming at providing organizations with informed decision making tools. However, these applications rely on providing quantitative information, rather than qualitative information that is relevant and intelligible for managers. In order to address these aspects, we propose a semi-supervised learning procedure that discovers and compiles information taken from online social media, organizing it in a scheme that can be strategically relevant. We illustrate our procedure using a case study where we collected and analysed the social media discourse of 43 organizations operating on the Higher Public Polytechnic Education Sector. During the analysis we created an “editorial model” that characterizes the posts in the area. We describe in detail the training and the execution of an ensemble of classifying algorithms. In this study we focus on the techniques used to increase the accuracy and stability of the classifiers.

Journalistic Relevance Classification in Social Network Messages: an Exploratory Approach

Social networks are becoming a wide repository of information, some of which may be of interest for general audiences. In this study we investigate which features may be extracted from single posts propagated throughout a social network, and that are indicative of its relevance, from a journalistic perspective. We then test these features with a set of supervised learning algorithms in order to evaluate our hypothesis. The main results indicate that if a text fragment is pointed out as being interesting, meaningful for the majority of people, reliable and with a wide scope, then it is more likely to be considered as relevant. This approach also presents promising results when validated with several well-known learning algorithms.