Distance Education professionals have been constantly coming up with methods and techniques to increase student participation in an environment where learning happens continuously and asynchronously. An online discussion forum (ODF) is one of these mechanisms, but it will only be successful if students are willing to participate. Stimulating students is a challenge many institutions currently face. The objective of this study was to analyze the social interaction among participants in ODFs using Social Network Analysis. Knowing the characteristics of these networks and its participants is important to design actions to improve the use of ODFs. As a case study, data were collected from ODF logs of the majors in Business Administration and Accounting in a Brazilian private university. This study found out that these interaction networks are sparse, which shows that students could be more engaged in interacting and collaborating with others. Students, in general, tend to interact more in the first semester and interaction diminishes as time passes. The number of active ODF participants has been around 45–50%, which shows that students currently do not participate very often in ODFs. Their main incentive seems to exist when they are graded. Popular ODFs were also analyzed.

This study characterized required skills and competences for data specialist roles by analysing job advertisements for data scientists and other related professionals. It was performed using a content analysis technique named centring resonance analysis (CRA). With the support of this technique, demanded skills were grouped into categories that allow a better understanding of each role as well as differences and similarities among roles were observed and analysed. This study also summarized our findings in an orientation framework to classify six data specialists' roles according to business and technical skills as well as to experience and educational demands. Professional experience seems, in general, to be more valued than academic background. This work sheds light on better differentiating job roles related to data science, which could guide companies that recruit such specialists by better defining job requirements. For universities, these findings support the development of new analytics and data science programs.

This article aims at characterizing the research community of Distance Education (DE) with respect to coauthorship, a special kind of collaboration among researchers, according to publications of 11 relevant DE journals. This article identified who the central researchers are, the topological properties of the coauthorship networks analyzed, the coauthorship patterns of each journal and the evolution of the DE community in the last 30 years. In order to achieve these goals, Social Network Analysis (SNA) was used, deriving centrality metrics, which depict the importance of researchers in the networks. This study found out that researchers who publish more papers are not necessarily the ones considered more central according to SNA. Besides, promising researchers, those linked to central researchers and are considered more likely to coauthor papers in the near future. Different coauthorship patterns among journals are described. Finally, a steep increase in the number of publications and coauthorships in the last decades was observed.

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