No abstract

Self-regulated learning rarely happens in isolation and although there is a wide range of evidence that socio-cognitive information may impact decision making and learning, its role in metacognitive self-regulation remains understudied. Thus, we investigated how socio-cognitive information on assumptions and confidence in assumptions of an unknown other in a learning setting affects (1) changes in assumptions (i.e., convergence towards other), (2) perception of the other’s competence, and (3) the search for information (i.e., metacognitive and conflict-based regulation). In our empirical study, N = 60 students first read texts, then judged statements as being true or false and stated their confidence in these assumptions on an integrated confidence/answer scale, were then confronted with bogus information on the supposed answers of another learner (computer generated), and were then asked to (1) re-state their assumptions including confidence judgment, (2) judge the other’s competence, and (3) decide what statements they wanted additional information on. Results showed that learners (1) converged towards the other learner, (2) judged the other’s competence in accordance with the other’s stated confidence, and (3) regulated their learning based on their own (adjusted) confidence rather than on the presence or absence of socio-cognitive conflicts. This suggests that socio-cognitive information can affect how learners metacognitively evaluate own assumptions and thus impact self-regulatory processes, but also that confidence of others affects their perceived competence. Although the direct impact of socio-cognitive conflicts on regulatory processes remains unclear, this study fosters our understanding of self-regulated learning processes within social contexts.

No abstract

The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski and Hecking, in: Benito, Cherifi, Cherifi, Moro, Rocha, Sales-Pardo (eds) Complex networks & their applications IX. Springer, Cham, pp 408–419, 2021) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that—in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps).