Luciana Oliveira

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.

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.

Measuring the return on communication investments on social media: The case of the higher education sector

Measuring the return on communication investments on social media has become one of the top key issues for organizations joining social networks. However, this field has been lacking articulation between what is conveyed as social media key performance indicators and the alignment of strategic organizational goals. Therefore, we propose a methodology to measure the performance of each organization on social media, to determine their positioning in the sector and to evaluate which are the content strategies used to boost the highest performing organizations. Thus, we identify how to determine which organizations should be closely monitored within the sector and which type content strategies can foster higher organizational performance on social media.

A System for Visualization and Analysis of Online Pedagogical Interactions

We present a system for a dynamic graphical representation of the interactions captured in educational online environments. The system goes beyond interaction between students and teachers, also addressing resource usage or any other entity for which it is possible to create a relation which binds two entities. By defining these relationships between pairs of entities in an online learning environment (Moodle, in our case) our tool creates a graph, where it is possible to apply techniques of social network analysis. This system brings up new possibilities for e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation and to find the implicit relations between participants, or participants and resources or events.

Social Network Analytics in Formal and Informal Learning Environments with EduBridge Social

The integration of social media in education has been raising new challenges for teachers, students and organizations, in both traditional and technology-mediated learnings settings. Formal higher education contexts are still mostly anchored and locked up in institutional LMS, despite the innumerous educational digressions that educators have been conducting throughout social media networks. One of the biggest challenges in contemporary educational needs consists on managing the integration, validation and reporting on educational processes, goals and student performance, when they are widely spread in several formal and informal contexts. In this chapter a system for the integration of LMS and social media is presented, as well as evidence on its practical usage. A set of social network analytics are also brought forward as features that are currently being added to the referred system.