A semantic approach to visualizing online conversations
Communications of the ACM - Supporting community and building social capital
Student-instructor communication: the role of email
Computers & Education
Visualisations for team learning: small teams working on long-term projects
CSCL'07 Proceedings of the 8th iternational conference on Computer supported collaborative learning
Exponential random graph modeling of communication networks to understand organizational crisis
Proceedings of the 49th SIGMIS annual conference on Computer personnel research
Are ILEs ready for the classroom? bringing teachers into the feedback loop
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
The big five and visualisations of team work activity
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Computational & Mathematical Organization Theory
Dynamics of email communications among university students throughout a semester
Computers & Education
Towards the development of multimodal action based assessment
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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With the advent of information technology, emails have gained wide acceptability among students as an asynchronous communication tool. According to the current pedagogy literature the overall trend of the use of email communication by university students has been increasing significantly since its inception, despite the rapid growth of the popularity and acceptability of other social mediums (e.g. Mobile phone and Facebook). In this study, we explore a longitudinal email communication network, which evolved under an increasing study load among 38 students throughout a university semester, using measures of social network analysis (SNA) and exponential random graph (ERG) models. This longitudinal network was divided into three waves, where each wave represents the portion of the complete longitudinal network that evolves between two consecutive observations. An increased study load was imposed through the assessment components of the course. SNA measures of degree centrality (i.e. the activity of an actor or actor popularity), betweenness centrality (i.e. the capacity to control the flow of information in a network), closeness centrality (i.e. reachable to other nodes) and reciprocity (i.e. tendency to make reciprocal links) are considered to explore this longitudinal network. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks. From the analysis of this email communication network, we notice that students' network positions and behaviours change with the changes in their study load. In particular, we find that (i) students make an increased number of email communications with the increase of study load; (ii) the email communication network become sparse with the increase of study load; and (iii) the 2-star parameter (a subset of three nodes in which one node is connected to each of the other two nodes) and the triangle parameter (a subset of three nodes in which each node is connected to the other two nodes) can effectively explain the formation of network in wave3; whereas, the 3-star parameter (a subset of four nodes in which one node is connected to each of other three nodes) can effectively explain the formation of network in wave1 and wave2. Interpretations of these findings for the monitoring of student behaviour in online learning environments, as well as the implications for the design of assessment and the use of asynchronous tools are discussed in this paper.