A combination of social network analysis and content analysis was used to investigate the interactions that took place in an online Moodle course among 22 participants that were enrolled in a teacher education graduate program. The purpose of this study was to examine both the patterns and quality of the online interactions, which were assessed using a content analysis schema. The social network measures of the online learning network demonstrated that Moodle may have the capability to help establish a cohesive online learning community where members are able to participate in meaningful social interactions. Results from the coding of discussion posts demonstrated that, despite active online interactions, participants still lacked higher level knowledge co-construction. Factors that might impede online knowledge construction processes are examined and discussed so that higher levels of knowledge construction can be encouraged and eventually realized.
Keywords: online interaction patterns, online learning community, knowledge construction, social constructivism learning theory, social network analysis, content analysis
Teachers’ Interactions in an Online Graduate Course on Moodle: A Social Network Analysis Perspective
The development of information and communication technology (ICT) has contributed to a transformation in traditional educational practices, and new approaches to 21st century learning have been evolving at a rapid pace. Online computer-mediated teaching and learning (e.g., online course management systems) is one new instructional approach that has emerged because of the advances in educational technology. One important reason for the rapid development of online courses is that they enable learners to learn “anytime, anywhere” (Tan, 2005, p. 1). Past research has recognized online learning for its capability of allowing learners who are separated by location, time, and space to learn together in a collaborative community (Hendriks & Maor, 2004; Richardson & Swan, 2003, as cited in Shen, Nuankhieo, Huang, Amelung, & Laffey, 2008).
Researchers have asserted that one of the most critical concepts in online learning is social interaction (Hendriks & Maor, 2004; Kumari, 2001; Picciano, 2002; Swan, 2003). Various studies provided validity evidence for the significant role that social interaction plays in traditional face-to-face learning environments (e.g., Brown & Duguid, 2000; Yalama & Aydin, 2004). These studies demonstrated that one of the most important factors that contributes to academic success in traditional learning environments is social interaction. Therefore, it is reasonable to assume that social interaction is equally, if not more, important in web-based learning environments. Indeed, researchers have agreed that social interaction is one of the most important features in online learning environments, as well as a key factor that contributes to the quality of students’ learning experiences (Kumari, 2001; Laman, Reever, & Scardmalia, 2001; Palloff & Pratt, 1999; Picciano, 2002; Shen et al., 2008; Sing & Khine, 2006). Wenger (1998) even claimed that the social and cognitive processes involved in online interactions are critical for an online community of learners who are constructing knowledge collaboratively.
In light of the critical role social interaction assumes in online learning, research in this area is needed to foster a better understanding of this phenomenon and to ultimately improve the practice of online education. A review of the literature revealed that most research on social interaction and online learning can be placed in two broad categories: (a) research using techniques such as interviews and surveys to examine students’ perceptions and attitudes toward online learning (e.g., Braun, 2008; Jiang & Ting, 2000), and (b) research using different schemas to code students’ online discussion posts (e.g., Hendriks & Maor, 2004; Kanuka & Anderson, 1998; Sing & Khine, 2006). While these data provide useful information for examining online learning, it is important that other dimensions of online social interactions also be examined to provide a more in-depth understanding of the unique features of online learning. Particularly, more research is needed to better understand both the complex social and complex cognitive processes that take place through online interaction.
These issues, however, are not completely understood because of various reasons. First, only a limited number of academic publications have addressed patterns of online interactions (e.g., Lipponen, Rahikainen, Lallimo, & Hakkarainen, 2003; Shen et al., 2008)—for instance, who is interacting with whom, how frequent the interaction is, and what positions students hold during social interaction. Second, although a few studies have provided different schemas to examine students’ cognitive processes of online learning in terms of the extent to which they co-construct new knowledge through social interaction, these studies focused on only a single dimension of students’ online interactions. The studies did not address how the patterns of students’ social interactions affect their cognitive construction of knowledge. Moreover, as Lipponen et al. (2003) noted, these studies do not employ multidimensional research methods in the research design. However, some researchers (e.g., Hmelo-Silver, 2003; Wegerif & Mercer, 1997) have pointed out that a multidimensional research approach is necessary in order to provide in-depth understanding of the process and quality of online interaction.
To address the gap in the literature, this study examines how a group of graduate students, who are also K-12 teachers from various subject areas, interacted with each other in a fully online Moodle course entitled Reading in the Content Areas. This study aimed to go beyond previous studies to address both the social and cognitive processes of online interaction by using a combination of social network analysis and content analysis, which allowed for the analysis of both quantitative and qualitative data. Moodle is becoming more popular, but its effectiveness has rarely been investigated. Accordingly, this study also aimed to provide insight into the effectiveness of Moodle at supporting social discourse and thereby enhancing co-construction of new knowledge.
Social Constructivism Learning Theory
This study used social constructivism learning theory (SCLT) as a central theoretical framework. SCLT presupposes that learning is both a social and cognitive process, which is mediated by frequent social interaction (Boudourides, 2003; Foko & Amory, 2008; Hendriks & Maor, 2004; Swan, 2003). In an SCLT environment, it is through interactive processes of discussion, negotiation, and sharing that effective learning takes place (Vygotsky, 1978). SCLT emphasizes that participants are active knowledge constructors, instead of passive individuals who just receive information from instructors or others (Wang, 2005; Zhu, 1996). Within this SCLT framework, social interaction is the core concept in any learning experience (Kumari, 2001; Picciano, 2002). A social constructivist learning environment can be well supported in the online learning environment, which has the unique feature of asynchronous online discussion forums (Swan, 2003). SCLT provides a sound theoretical framework for understanding the process of collaborative knowledge construction in a computer-mediated learning environment (Coe et al., 2004). Indeed, this theory has been applied extensively in studies investigating online learning environments (e.g., Kanuka & Anderson, 1998; Lipponen et al., 2003; Sing & Khine, 2006; Wang, 2005).
Social Network Analysis
Social network analysis (SNA) focuses on patterns of relations between individuals in social networks (Carrington, Scott, & Wasserman, 2005). SNA enables one to detect the interactions and relations among network members, describe the patterns of interactions, and trace how information flows within the network (Knoke & Kuklinski, 1982). This research method is popular in other disciplines (e.g., sociology and anthropology); however, there has been a growing interest in applying SNA to education to investigate the interactions among students. SNA provides a useful technique to study the social construction of knowledge (Vera & Schupp, 2006).
One important advantage of using SNA is that researchers can visualize and quantify the interaction patterns of learners, as well as figure out how the social interactions of participants influence the construction of new knowledge. Common SNA measures include (a) density of the network, (b) centralization, and (c) centrality measures. Density refers to the extent to which all the nodes (e.g., persons, students) in the network are connected with each other. The density of a binary network refers to the total number of ties (or connections) among the nodes, and is expressed as a proportion of the maximum amount of possible ties. The value of this measure ranges from 0 to 1; the higher the value, the more established the learning community (Scott, 1991). Centralization refers to the extent to which the social network graphic is organized around the most central points (Tuire & Erno, 2001). Centrality measures in this study include Freeman’s degree and betweenness. The degree is the number of other nodes to which a node is connected (Scott, 1991). For this study, as the matrix is directed, we calculated both the in-degree and out-degree for each student. Lastly, betweenness refers to the extent to which a particular node lies between the various other nodes in the graphic. (See Scott , among others, for information on SNA.)