Engaging students in peer assessment is an innovative assessment process which has a positive impact on students learning experience. However, the adoption of peer assessment can be slow and uncomfortably experienced by students. Moreover, peer assessment can be prone to several biases. In this paper, we argue that the analysis of peer assessment interactions and phenomena can benefit from the social network analysis domain. We applied a graphlet-based method to a dataset collected during in-class courses integrating a peer assessment platform. This allowed for the interpretation of networking structures shaping the peer assessment interactions, leading for the description of consequent peer assessment roles and their temporal dynamics. Results showed that students develop a positive tendency towards adopting the peer assessment process, and engage gradually with well-balanced roles, even though, initially they choose mostly to be assessed by teachers and more likely by peers they know. This study contributes to research insights into peer assessment learning analytics, and motivates future work to scaffold peer learning in similar contexts.

Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to choose in each particular case the most suitable algorithm which would provide the richest insights into the structure of the social network they study. Through a case study of the French crowdfunding platform, Ulule, this paper demonstrates an original methodology for the selection of a relevant algorithm. For this purpose we, firstly, compare the partitions of 11 well-known algorithms. Then, bivariate map based on hub dominance and transitivity is used to identify the partitions which unveil communities with the most interesting size and internal topologies. These steps result in three community detection methods relevant for our data. Finally, we add the socioeconomic indicators, meaningful in the framework of the crowdfunding platform, in order to select the most significant algorithm of community detection, and to analyze the cooperation patterns among the platform’s users and their impact on success of fundraising campaigns. In line with previous socioeconomic studies, we demonstrate that the social concept of homophily in online groups really matters. In addition, our approach puts in light that crowdfunding groups may benefit from diversity.