Citekey: @Laat2007

de Laat, M., Lally, V., Lipponen, L., & Simons, R.-J. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. International Journal of Computer-Supported Collaborative Learning, 2(1), 87–103. doi:10.1007/s11412-007-9006-4


This paper is about applying SNA in analyzing CSCL (or networked learning as called in EU). What is unique about this study is it highlights the value of combining SNA with Content Analysis together, and put them both onto a timeline (considering the temporal dimension), to more holistically analyze CSCL processes. In particular, while SNA provides behavioral accounts of interactions (e.g., density of an interaction network and centrality of its individuals), Content Analysis further adds insights about what those behaviors/interactions are all about. In the authors’ case, CA codes ‘learning’ and ‘tutoring’ processes in forum posts, whereas SNA provides a picture (or pictures) about the resulting network. While by putting SNA onto a timeline can reveal changes with interactions, insights from CA help explain these changes by putting corresponding learning/tutoring processes into perspective. Overall, this paper highlights the importance of combining insights from multiple analyses, including CA, SNA, and temporal analysis when understanding CSCL.

Also, this paper provides a quite broad review of applying SNA to analyzing CSCL.


The aim is to use these methods to study the nature of the interaction patterns within a networked learning community (NLC), and the way its members share and construct knowledge. The paper also examines some of the current findings of SNA analysis work elsewhere in the literature, and discusses future prospects for SNA. (p. 1)

social network (Wellman, 2001) (p. 2)

As Barry Wellman indicated in the magazine Science, “human computer interaction has become socialized. Much of the discussion […] is about how people use computers to relate to each other… [and] has slowly moved from the lone computer user to dealing with (1) how two people relate to each other online, (2) how small groups interact, and (3) how large unbounded systems operate.” (Wellman, 2001, p. 2031). (p. 2)

SNA is a research methodology that seeks to identify underlying patterns of social relations based on the way actors are connected with each other (Scott, 1991; Wasserman & Faust, 1997). (p. 2)

1 NL is a U.K. and European term that is used in place of CSCL. We think it is, for practical purposes, synonymous with CSCL and henceforth will refer to them both as NL/CSCL. (p. 2)

we will discuss (1) how SNA can be used, in general terms, when studying NL/CSCL, and (2) provide a brief account of the application of SNA to a small sample of our own case study data. (p. 3)

A general discussion of Social Network Analysis and NL/CSCL (p. 3)

The use of content analysis (Gunawardena et al., 1997; Hara, Bonk, & Angeli, 2000; Henri, 1992) can provide insight into the nature of the content of communication among the participants. This can then augment the perspective gained by using SNA to focus on network connections. These may vary in content, in direction of information flow, and in strength (network connections can be weak or strong, depending on the number of exchanges between participants). (p. 3)

The two key indicators of SNA are “density” and “centrality.” Density provides a measure of the overall ‘connections’ between the participants. (p. 4)

Centrality is a measure that provides us with information about the behavior of individual participants within a network. (p. 4)

A sociogram is a representation of all participant connections in a social network. (p. 4)

We will now briefly summarize some recent studies in NL/CSCL that have made use of SNA. (p. 4)

Haythornthwaite, for example, showed that during class communication in a NL/CSCL environment there was a tendency to interact more as teams within the network. Martinez, Dimitriadis, Rubia, Gomez, and de la Fuente (2003) found that the density of a network was affected by the teacher’s presence. Reffay & Chanier (2003) illustrated that SNA can help study the cohesion of small groups engaged in collaborative distance learning as a way to locate isolated participants, active subgroups, and various roles of the participants in the interaction structure. Reuven, Zippy, Gilad, and Aviva (2003) found that in a structured, asynchronous learning network (as opposed to an unstructured open discussion forum) the knowledge construction process reached a high level of critical thinking and the participants developed cohesive cliques. Nurmela et al. (1999) used SNA to study participation in collaborative learning activities such as knowledge building and acquisition. Cho, Stefano, and Gay (2002) used SNA techniques in an educational context to identify central, influential actors in a class. They found, similarly to Beck, Fitzgerald, and Pauksztat (2003), that participants using a discussion board were more likely to follow recommendations made by highly ‘central’ actors than those made by peripheral actors. Daradoumis, (p. 4)

Martinez-Mones, and Xhafa (2004) used SNA to assess participatory aspects, identify the most effective groups and most prominent actors to monitor and assess the performance of virtual learning groups. (p. 5)

An example of SNA as part of a multi-method case study (p. 5)

In the second part of this paper we will present a brief summary of one of our own case studies as an example of how SNA may be used to explore group cohesion and interaction patterns within a networked learning community. (p. 5)

illustrate how these patterns evolve over time, and attempts to combine these outcomes with the development of teaching and learning processes (p. 5)

the notion of following interaction patterns over time within Networked Learning Communities has been implemented in several studies. Hara et al. (2000) provide a study in which they conduct a timeline analysis of computer-mediated communication. Howell-Richardson & Mellar (1996) made weekly visual representations of conference activity, based on direct or indirect connections made by the students in their messages. Their analysis was focused on describing interaction patterns when students are assigned to particular roles, and exploring these patterns as they changed over time. Daradoumis et al. (2004) implemented a similar time-line analysis in their research design to track the changes in student participation and group cohesion over time. (p. 5)

Haythornthwaite (2001) and Martinez et al. (2003) also concluded that network patterns change and that it is important to study these changes over time. (p. 5)

To summarize, in this study we focus on the following questions: 1. How dense is participation within the network and how does this change over time? 2. To what extent are members participating in the discourse and how does this change over time? (p. 5)

The program is hosted in the virtual learning environment called WebCT. (p. 6)

Our analysis is based on collaborative project work conducted by seven students and one tutor in the first workshop of this program (approximately 10 week’s duration). In order to make the analysis manageable we sampled the message data from the workshop (approximately 1,000 messages were posted during the task). We divided the 10-week period into three sections: beginning, middle and end. (p. 6)

The central purpose of content analysis (CA) is to generalize and abstract from the complexity of the original messages in order to look, in our case, for evidence of learning and tutoring activities. (p. 6)

The first coding schema, developed by Veldhuis-Diermanse (2002), was used to code units of meaning that were regarded as “on the task.” These focused on the learning processes used to carry out the task. This schema includes four main categories: cognitive activities used to process the learning content and to attain learning goals; metacognitive knowledge and metacognitive skills used to regulate the cognitive activities; affective activities used to cope with feelings occurring during learning; and, finally, miscellaneous activities. (p. 6)

The second schema is used to code units of meaning that are “around the task,” where the focus is on tutoring (Anderson et al., 2001). This schema includes three main sub–categories: design and organization, facilitation of discourse, and direct instruction. (p. 6)

Using WebCT as a source of raw data for SNA (p. 8)

The information retrieved from WebCT logs (p. 8)

Sample results of SNA analysis (p. 8)

In the beginning phase, the density is 48%, and for the middle phase the value is 46%. In the last phase of the collaboration the value drops somewhat, to 36%. (p. 8)

A high out-degree centralization value indicates that the communication is dominated by some central participants; a low value means that communication is distributed more equally among all the participants. It is interesting to see that while the density drops slightly in the middle, the out-degree centralization goes up. This means that some participants have become more centrally involved compared to the beginning phase. (p. 8)

In general, this imbalance does not necessarily mean that some participants control the communication by excluding others. It may mean that some participants choose to make fewer contributions to the community during this phase. (p. 8)

To explore some answers to the second question, (To what extent are members participating in the discourse and how does this change over time?), we start by presenting the findings of the inand out-degree values for each participant (p. 9)

Using SNA alone, however, does not provide us with a full picture. It is also useful to combine these findings with the outcomes of content analysis to interpret whether central participants, as determined by SNA, are also central to the learning and teaching activity within this group. (p. 9)

When relating the SNA results to the CA (Table 3) we see that Andrea and Charles are responsible for 60% of the learning messages, and that the others (except Bill) are also making a learning input. With respect to tutoring, it seems that Brian and Charles (50%) are responsible for most of this, but all the participants were involved to some extent as well. This leads to the conclusion that although Brian, Andrea and Charles appear to be active participants, they are not entirely dominating the teaching and learning activities of the NLC during this phase. (p. 9)

These general network properties can also be studied more closely using the sociograms. (p. 11)

Conclusion and discussion (p. 13)

At the present time, the number of studies available that adopt a research agenda similar to the one outlined here is relatively small. (p. 14)

It is therefore crucial to use a combination of content analysis, interviews and social network analysis to understand the teaching and learning processes that are present during NL/CSCL. (p. 14)

In summary, what do these social network analysis diagrams and network properties add to what we already know, from previous research (de Laat & Lally, 2003, 2004), about this community? The overall patterns of communication are illustrated in a way that shows the social nature of group learning and tutoring. This dimension was not revealed in content analysis of messages (Table 3) and CER. The diagrams show how people connect to the members in the group, the patterns of collaboration are revealed (one-to-one or many-tomany), and the involvement of individuals in each phase. The findings may be used to seek further explanation for this behavior or can be used to contextualize previous findings about the NL/CSCL activities. However, only by combining SNA with CER and CA can we understand the process and intentions of the participants at the level of individual agencywhat they claim they are doing, why they are doing it and how it occurs through posted messages. By using a time line analysis when studying learning and teaching processes we can also see how certain participants become gradually more active and central figures in their community. (p. 15)

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