Citekey: @Lee2016-ys

Lee, J., & Bonk, C. J. (2016). Social network analysis of peer relationships and online interactions in a blended class using blogs. The Internet and Higher Education, 28, 35–44.


An interesting study exploring the relationships between perceived closeness between students and their actual interactions on blogs. Some explanations of SNA are questionable, so are the interpretation of several SNA results. For instance, comparisons of network measures over time should come after an explicit explanation of network construction (e.g., sliding window, accumulation).


This study examines the social network of the learner relationships and online interactions in a graduate course using weblogs for writing and sharing weekly reflective journals during a 16-week semester. The social network data of the learner relationships were gathered twice by measuring learners’ perceived emotional closeness with other learners. In terms of the online interactions among the learners, the numbers of replies that individual learners had posted to and received from others’ postings were respectively calculated and analyzed. The findings from these measures indicated that the social network patterns and values as measured by peer relationships were noticeably changed at the end of the semester, when compared to that at the beginning. (p. 35)

A review of empirical studies of blogs (Sim & Hew, 2010) reported that there have been two major research topics in this field: (1) describing the usage profile of blogs, and (2) examining the effects of blogging. (p. 35)

In general, learning psychologists have found that the more learners reflect and elaborate on newly learned content, the more they will remember later (Driscoll, 2005). (p. 35)

The purpose of the current study is two-fold. First, the researchers sought to describe the network structure of the learner relationships in terms of perceived closeness and online interactions (p. 35)

Second, they attempted to identify any relationships that existed between the degree of perceived closeness and that of social interactions. (p. 36)

Worse still, as indicated, such course hub sites are often unavailable after the course ends or taken offline after a few years. Compounding the problem, it is not uncommon for an institution to switch to a new LMS system. When this happens, all the previous learner contributions and indications of learning are wiped clean as the system is taken offline. As a result, any powerful learning episodes vanish or may no longer be accessible. (p. 36)

  1. Did the social network structure of peer relationships change between at the beginning and at the end of the course using blogs? 2. How did the social network of online interactions form through blogging? 3. Isthereasignificantrelationshipbetweentheonlineinteractionsand the peer relationships? (p. 36)

In contrast, individually owned blogs are lasting and accessible until a user decides to close her blog. (p. 36)

  1. Literature review (p. 36)

2.1. Blogs: individual publishing system with sense of ownership (p. 36)

Ownership of a blog also shifts the balance of power and control in a course to the learners (Kang, Bonk, & Kim, 2011). (p. 36)

Most fundamentally, the read-write Web dramatically enhances the power of individuals and fosters a participatory culture of building, tinkering, learning, and sharing (Brown, 2006). (p. 36)

2.2. Interaction and peer relationship in blogs (p. 36)

Interactivity is regarded as critical to the success of social network systems (Williams & Jacobs, 2004). (p. 36)

2.4. Social network analysis (SNA) in education (p. 37)

A social network can be defined as “a specific set of linkages among a defined set of persons” (Wasserman & Faust, 1994, p. 2). Social network analysis (SNA) is the analytical method of social networks, which views social networks as relationships in a group, consisting of nodes (agents) and links (relationship). (p. 37)

However, interaction does not guarantee positive educational goals. The quality of interactions often matter. Cutler (1995) said “the more one discloses personal information, the more others will reciprocate and the more individuals know about each other, the more likely they are to establish trust, seek support, and thus find satisfaction” (p. 326). Tinto (1993) observes social interaction positively affects persistence in college. Interaction among learners supports the learning process (Rovai, 2002) and learning takes place through the sharing of purposeful activity (Lave & Wenger, 1991). In addition, Dawson (2006) found that students with frequent interaction possess stronger sense of community. (p. 37)

SNA has commonly been used as a major data analysis method in educational research to illustrate different variables such as relationship, emotion, help, and other social phenomena. (p. 37)

SNA does not deal with attribute data of individuals, but with relational data “that cannot be reduced to the properties of the individual agents themselves (Scott, 2013, p. 3).” It is believed that social structures are built from relations. (p. 37)

It is argued that good peer relationships provide individuals with social support and belongingness that can foster deeper and richer learning experiences (Rogers, 1983). In short, peer relationships contribute to academic performance indirectly by way of motivation and emotional support. (p. 37)

2.3. Blogging for reflection (p. 37)

Reflection fosters the transfer of learning (Bouldin, Holmes, & Fortenberry, 2006). (p. 37)

  1. Methodology (p. 37)

A software tool called NetMiner 4 was used to analyze the social network of peer relationships and online interactions (p. 39)

4.3. Coreness in the peer closeness network (p. 40)

Figs. 4 and 5 display the coreness maps at the beginning and end of the course, respectively. In Fig. 4 the nodes located in the core at the start of the semester were all students majoring in instructional systems technology. Not surprisingly, they were relatively closer than the others in the visual depiction. However, at the end of the semester, as displayed in Fig. 5, other than three students (AJY, BHK, JBS), most students became located close to the core. (p. 40)

  1. Results (p. 40)

In terms of peer relationship, the network density in the second week was 0.17 (# of links = 78), which indicates a loosely tied network (see Fig. 2). The network density value measured in the final week increased to 0.62 (# of links = 285), which revealed that the students had become much more strongly tied to each other (see Fig. 3). (p. 40)

unclear whether the links were accumulated throughout the course. If so, this increase tells nothing. The numbers will naturally grow given more student interactions. (p. 40)

4.6. Correlation between online interactions and peer closeness perceived (p. 42)

Interestingly, all the centrality values regarding online interactions and peer closeness were statistically significant (see Table 6). (p. 42)

A paired t-test was used to determine if there was any statistically significant difference between the beginning and final weeks in terms of degree centrality values about peer closeness. Significant differences in peer closeness were found in both in-degree (t = −.16.83, p b .01) and out-degree (t = −8.30, p b .01) values (see Table 4). The overall centrality of individual students significantly increased from the beginning. (p. 42)

  1. Conclusion and discussion (p. 42)

Several key findings of this study have wide ranging implications for instructors designing online courses using blogs. (p. 42)

5.2. Future directions (p. 43)

Future studies might explore the correlation between social network values of peer relationships and individual students’ academic achievement. (p. 43)

Rovai and Jordan (2004) provided empirical evidence that blended courses produce a stronger sense of community than either face-toface or fully online courses. However, several students in the present study reported that they experienced that close feeling with other peers online through blogging but that it has not transferred to the face-to-face classrooms. (p. 43)

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