Are forum networks social networks? A methodological perspective

Abstract

The mission of learning analytics (LA) is to improve learner experiences using the insights from digitally collected learner data. While some areas of LA are maturing, this is not consistent across all LA specialisations. For instance, LA for social learning lack validated approaches to account for the effects of cross-course variability in learner behavior. Although the associations between network structure and learning outcomes have been examined in the context of online forums, it remains unclear whether such associations represent bona fide social effects, or merely reflect heterogeneity in individual posting behavior, leading to seemingly complex but artefactual social network structures. We argue that to start addressing this issue, posting activity should be explicitly included and modelled in forum network representations. To gain insight to what extent learner degree and edge weight are merely derivatives of learner activity, we construct random models that control for the level of posting and post properties, such as popularity and thread hierarchy level. Analysis of forum networks in twenty online courses presented in this paper demonstrates that individual posting behavior is highly predictive of both the breadth (degree) and frequency (strength) in forum communication networks. This implies that, in the context of forum-based modelling, degree and frequency may not reflect the social dynamics. However, results suggest that clustering of the network structure is not a derivative of individual posting behaviour. Hence, weighted local clustering coefficient may be a better proxy for social relationships. The empirical results are relevant to scientists interested in social interactions and learner networks in digital learning, and more generally to researchers interested in deriving informative social network models from online forums.

Reviewing Theoretical and Generalizable Text Network Analysis: Forma Mentis Networks in Cognitive Science

“…Recommendations for network studies put forward by the participants of NetSciLA21 workshop similarly emphasized that when researchers construct networks in learning settings, they need to carefully define nodes and relationships for the network representation. How to operationalize networks of people who interact in learning settings had been discussed elsewhere [9], [10]. Here, we further argue that scrupulous methodological and theoretical considerations are less common when learning analytics researchers conduct network analysis of text.…”

Video Features, Engagement, and Patterns of Collective Attention Allocation

“…In learning analytics studies, research has relied heavily on social network models to interpret interactions between people, assuming their behaviours will be influenced by others in their neighbourhoods, such as friends, peers, and classmates (e.g., Joksimović et al, 2016;Oleksandra & Shane, 2016;Gillani & Eynon, 2014). Social network analysis has promised to significantly change the way we examine the nature of online learning behaviours that mainly occur in forums (e.g., Hernández-García et al, 2015;Poquet, Tupikina et al, 2020;Kellogg et al, 2014;Stepanyan et al, 2013;Zhang, Skryabin et al, 2016;Poquet & Jovanovic,2020;. The social network model is a powerful, useful tool for examining relationships between actors.…”

Cacophony of Networks in Learning Analytics

“…Some studies have examined university online courses [16] where groups of learners are bounded by similar motivation, similar curriculum trajectories, and likely higher homogeneity in prior knowledge. Other studies focused on MOOCs [28] where learners heterogeneous in their motivation and prior knowledge are bounded by course enrolment, but their patterns of social participation and commitment are fluid [45]. Network analysis has also been applied to open-ended social contexts where group boundaries are ill-defined, to inquire into informal learning in open Internet communities [20,34].…”

“…In addition, network-level analyses are applied in epistemic network analysis (ENA, see [48]), a particular methodology that represents epistemic views of individuals and groups as network structures to demonstrate similarities and differences between them. Using network-level studies in LA, researchers also have statistically modelled online learner networks to describe the mechanisms that can explain what drives the formation of network ties [29,45].…”