SOCIOLOGY OF STUDENT-CENTRED E-LEARNING COMMUNITIES: A NETWORK ANALYSIS

Transcription

1 SOCIOLOGY OF STUDENT-CENTRED E-LEARNING COMMUNITIES: A NETWORK ANALYSIS Andrew Laghos Centre for HCID, City University London, UK dr847@soi.city.ac.uk Panayiotis Zaphiris Centre for HCID, City University London, UK zaphiri@soi.city.ac.uk ABSTRACT In traditional face-to-face learning environments, the students are part of a classroom where there is a teacher who presents the lectures and supervises the communication interactions. But what happens when the learning is online and there is no teacher to mentor the process? In this paper, we have analyzed a self-taught e-learning community of students who are taking part in an online language course. Our results show that the students relied on peer-to-peer support for their course-related questions and to socialize with their fellow students. Furthermore, through our analysis, we have identified one of the students to have taken on the role of a teacher for this e-learning course. KEYWORDS e-learning Communities, Sociology, Asynchronous Communication, Social Network Analysis, Topic Relation Analysis. 1. INTRODUCTION As the popularity of the Internet keeps increasing, so do the numbers and sizes of online communities. In our study we are interested about the online communities that form around e-learning courses that are available on the World Wide Web. When the students of the courses engage in Computer-Mediated-Communication (CMC) activities (like participating in discussion forums) online communities emerge (also referred to as cyber societies and virtual communities among several others). Virtual communities are social aggregations that emerge from the Net when enough people carry on those public discussions long enough, with sufficient human feeling, to form webs of personal relationships in cyberspace (Rheingold, 1993, pp.5). Preece, Rogers and Sharp (2002) add that an online community consists of people, a shared purpose, policies and computer systems. Analyzing and understanding these webs of human interaction assists web-developers and course maintainers to provide the technical and course material related requirements for the students of their course. Furthermore, an in-depth analysis of these social networks allows for actions to be taken to influence the students CMC participation. In this paper we are using a method called Social Network Analysis (SNA) to analyze the communication that takes place between the students of an online language-learning course. Our aim was two-fold. First, to explore the use of SNA in CMC analysis, and then to apply it to a specific case-study with the goal of investigating what roles the students take in a non-administered self-taught e-learning course. The paper begins by introducing Social Network Analysis and providing a description of our case-study course. Then we detail the methodology of our study and present and discuss the results. Finally, the paper concludes with future research directions. Our attempt throughout the paper was to both describe our work but also provide a basis (through definitions and quantitative results) that others could use for similar work.

2 2. SOCIAL NETWORK ANALYSIS Social Network Analysis (SNA) is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships (Krebs, 2004, pp.1). Preece (2000) adds that it provides a philosophy and set of techniques for understanding how people and groups relate to each other, and has been used extensively by sociologists (Wellman, 1997), communication researchers (Rice, 1994) and others. Analysts use SNA to determine if a network is tightly bounded diversified or constricted, to find its density and clustering, and to study how the behaviour of network members is affected by their positions and connections (Scott, 2000; Knoke & Kuklinski, 1982). The aim of social network analysis is to describe why people communicate individually or in groups (Preece, 2000, pp. 183), while the goals of SNA are to visualize relationships/communication between people and/or groups using diagrams, to study the factors which influence relationships and the correlations between them, to draw out implications of the relational data, and to make recommendations to improve communication and workflow (Dekker, 2002). SNA data is represented using matrices, graphs and sociograms. SNA is useful for describing relations based on traditional synchronous communications like face-to-face and telephones, and extending them to online asynchronous communication is a significant step (Aviv, Erlich & Ravid 2003). Our case study was a self-taught e-learning course for learning the Modern Greek Language and is called Learn Greek Online (LGO). LGO was built through participatory design and distributed constructionism (Zaphiris & Zacharia, 2001). The course is hosted on Kypros-Net Inc. (Kypros-Net Inc, 2005), a non-profit organization for the promotion of the culture and language of Cyprus. It uses the Moodle (Dougiamas, 2001) open source course management system. LGO is not a required course. The students enroll on their own will and their CMC participation is completely voluntary. Unlike other courses where the students are required to participate in the discussions allowing for experimental bias, LGO students contribute to the discussions because they want to and not because they have to. The students of the include people with no knowledge of the Greek language, as well as bilingual members of the Greek Diaspora. The site consists of seven modules (Greek 101 (Elementary) to Greek 302 (Advanced)). We have carried out SNA on the students who used the discussion forums of the Greek 101 (Elementary) module of LGO, and tabulated these interactions in the form of a network matrix. To carry out the social network analysis we used an SNA tool called NetMiner for Windows (Cyram, 2004). Due to the complexity of the interactions in the LGO discussion we had to make several assumptions in our analysis: Posts that received 0 replies were excluded from the analysis. This was necessary in order to obtain meaningful visualizations of interaction. Open posts were assumed to be directed to everyone who replied. Replies were directed to all the existing actors of the specific discussion thread unless the reply or post was specifically directed to a particular actor. First, the basic network properties of LGO were obtained. There were 618 students in the module and contributed to 18,025 communication links that are characterized by direction and strength (De Nooy and Batagelj., 2005). The density of the LGO network (0.047) is the ratio of the number of links present in the network, to the maximum possible links. Inclusiveness is the number of connected points expressed as a proportion of the total number of points (Roberts Jr., 2000). In this case inclusiveness is 1, which means that by the end of the course, all of the studied students had posted at least one message each in the discussion board. Following this, we analysed the connection, centrality, cohesion and equivalence of the LGO network. 2.1 Connection In the LGO course, there were 15 lessons - each with its own discussion board. Beginning from Lesson 1 we carried out the various types of SNA up until Lesson 15. This enabled us to follow closely the formation and evolution of our network. Figures 1 and 2 are sociograms that show the structure of the state of the LGO network in Lesson 1 and Lesson 15 respectively. The nodes represent the students (the student names have been renamed S1 S618 for privacy and anonymity reasons), while the strands between them represent their interactions. When there is a line directly connecting two nodes then these nodes are adjacent. When a node is one of a pair of nodes defining the line then this node is incident to the line. The number of lines that are incident with it is called nodal degree (Wasserman and Faust, 1994). In-degree is the number of lines that are

3 Figure 1. Lesson 1 Local Connection. Figure 2. Lesson 15 Local Connection. incident to a node while out-degree is the number of lines that are incident from it. In LGO the degree values for both (in and out) is Nodes whose degree equals 0 are called isolates. In the first instance (figure 1) it is clear that many students have no links and are isolated in the network. As the course evolved more students engaged in the discussions and finally, as depicted in figure 2, every student ended the course with at least one connection with another student.

4 An Ego network consists of a focal node and a set of alter nodes adjacent to or from the focal node (Burt,1992). Ego-net size is defined as the number of nodes that are adjacent to or from a focal node (Burt, 1992). Mean value for ego-net size in LGO is The majority of the links in LGO were posted in Lesson 1 (15583) and rose through the lessons (18025). Dependency measures how student i is dependent to student j when going to other nodes and is computed by the in betweenness centrality s process (Freeman, 1980). For LGO the in and out dependency values are equal, with an increasing trend from lesson 1 (1.008) to lesson 15 (1.491). Link connectivity (10.078) is the minimum number of links that must be removed to make the network disconnected. The indirect connectedness among nodes in a network is analyzed by the shortest path where path is defined as a sequence of nodes and links where all nodes and links are distinct (Floyd, 1962). The length of shortest path between two nodes is the geodesic distance and in the LGO network it averages to If a path exists between two nodes they are said to be reachable. The mean value for reachable nodes in LGO is (out of the 618 students). A bridge is a link in a network that if deleted along with any incident nodes, increase the number of connected components, while a cutpoint also called an articulation node is a node that if deleted along with any incident links, increases the number of connected nodes (Harary, 1969). Our analysis shows that a slight increase occurs in the number of bridges from lesson1 to lesson15, while the number of cutpoints remains fairly constant ranging from 34 to 37. Overall through the evolution of the course, the mean link connectivity of the network increases suggesting that the network becomes more connected lesson by lesson since more links must be removed to disconnect the network. The mean geodeisic distance slightly decreases from 2.63 to while at the same time the average number of reachable nodes increased by around 53% indicating that the network has become more connected. 2.2 Centrality Degree centrality is measured by the portion of nodes that are adjacent to each node. The nodes with the highest degree scores are the ones who are more central (powerful) in the network. In a directed network like the LGO case, the in-degree centrality is the portion of nodes that are adjacent to each node, and out-degree centrality is the portion of nodes that are adjacent from each node (Freeman, 1979). In LGO the mean centrality in-degree and out-degree values increase from lesson 1 (0.041) to lesson 15 (0.047). We also took a more in-depth look at the evolution of the centrality degree in the course. Table 1 summarizes the in and out centrality degree values for the LGO network. It can be seen that the mean in and out values are equal, while the standard deviation, min and max values per lesson only slightly differ between in and out. Table 1. Evolution of in-degree and out-degree centrality in LGO. In-Degree Out-Degree Mean Std. Dev. Min Max Mean Std. Dev. Min Max L L L L L L L L L L L L L L L

5 Through the evolution of the course, the average centrality degree increased but only slightly, indicating that the more central students had gained and maintained their powerful status early on in the course. This can also be seen from figures 3 and 4, which are the sociograms of the mean out-degree values for lessons 1 and 15 of LGO. S7 is clearly the most powerful student of the course and became as such from the very first lesson. Students like S45, S111, S92, S91, S61 are not as central as S7 but they are the ones present in an outer cycle of the sociogram, whereas the majority of the students are in the farthest grid circles of the sociogram and thus have the least power in the LGO network. Figure 3. Lesson 1 out-degree centrality. Figure 4. Lesson 15 out-degree centrality.

6 2.3 Cohesion A clique is a maximal complete subgraph of three or more nodes consisting of a subset of nodes which are adjacent to each other, and there are no other nodes in the network that are also adjacent to all of the members of the clique. Cliques may overlap, meaning a node can be a member of more than one clique (Bock and Husain, 1950). In the LGO case, we have carried out the evolutionary clique analysis on cliques with a minimum number of 3, 5, 10, 20, 50 and 100 members and the results can be seen in table 2. The clique that had over 50 members - which is therefore the same clique that had 100+ members since there was only 1 - had developed since lesson1 and remained throughout the duration of the course. The cliques with 10+ and 20+ students increased gradually from lesson to lesson. Cliques with 3+ and 5+ members kept on increasing in size with every lesson with the exception of one a 5+ member clique being dropped in lesson13. The results indicate that communication in the social network remained active and more and more students would interact with peers they hadn t exchanged messages with before. 2.4 Equivalence Table 2. Evolution of LGO Cliques. Cliques with at least n number of members L L L L L L L L L L L L L L L Equivalence of the network members shows when two actors have similar patterns of relations. The two main types of equivalence are structural and regular. The structural equivalence analyzes the role-set structure of a network based on the similarity of tie-profiles among its nodes and is computed by the Euclidean distance of tie-value from and to all other nodes (Lorrain and White, 1971). Two nodes are said to be structurally equivalent if they have identical ties with themselves, each other and all other vertices (de Nooy et al., 2005). The mean value for structural equivalence in LGO was When two nodes have the same profile of ties with members of other sets of actors that are regularly equivalent, then they are themselves regularly equivalent. These nodes do not have to be connected to the same vertices, but have to be connected to vertices in the same classes (de Nooy et al., 2005). The algorithm we used to calculate the regular equivalence is Borgatti and Everett s CatRE (Everett and Borgatti, 1993) and the value was for the LGO network. Network roles are suggested by similarities in network members behavior. The Local role was calculated using Winship and Mandel's measure which states that nodes i and j are more role equivalent if the collection of ways in which actor i relates to others is more the same as the collection of ways in which actor j relates to others (Winship and Mandel, 1983). The mean value of local role was in LGO. The structural equivalence in the LGO network in lesson1 (10.894) gradually increases to by lesson 15.

7 This increase by 16% suggests that although the similarity between the students communication was formed from the beginning of the course, it became even more similar by the end of the course. Figure 5. Lesson 15 Structural Profile. Finally, figure 5 illustrates the role of the students in the form of a structural profile sociogram. The actors that are closer to each other are the ones with the most similar patterns of communication. The most active students (S7, S157) had separated themselves from the others as early as lesson1. As can be seen, S7 is the actor furthest away from all the other nodes suggesting that his style of interaction in LGO was unique from the rest of the student. The other students are dispersed around, but are much closer to each other in comparison to the more active individuals. During progression of the course however, these dispersed students have moved closer to each other forming clusters of students. This tells us that some groups of students had very similar interaction patters amongst them. By the end of the course, four main roles have been identified and these have been grouped and named R1, R2, R3 and R4 (circled in figure 5). In R1 we only have one student, namely S7, and in R2 it is S157. These two students interaction patterns are so distinct from those of the rest of the students, so they are in a group of their own. R3 is a cluster of students with S12 and S2 managing to separate themselves a bit from the rest of the students in the cluster. Finally R4 is composed of another cluster of students where the only member who is not as similar as the rest is S61. First indications show that the students in group R4 are the least active in the module and do not contribute much to the discussion boards. In R3 however we have the group of the more vocal students. The student in R2 is separate from those of R3 since he/she has a much higher frequency of participation than them. We found R1 to be the most interesting to study because he/she is in the farthest corner of the structural profile and further away from the rest of the students (figure 5). For the purposes of this study, we have focused on R1. We collected information about the only student in R1, including his/her ego-net social network analysis measures, as well as a content analysis on the topics he/she would participate in and contributed to. 3. DISCUSSION Following the SNA study, we had identified S7 to be the most active student in the LGO discussion boards. In order to create a communication persona for this role group, we studied S7 in more detail. First, we did an ego-centred analysis on the student to obtain his/her individuals SNA scores. The in-degree and out-degree centrality score of S7 was 54.8% of the total contributions from all students in the course. This shows that S7

8 was involved in a little more than half of the overall communication that took place in the course, in both incoming and outgoing exchanges. His neighbour out and in degree were 338 messages sent and 338 messages delivered which was the highest out of all the students in the network. In table 3 we can see how many cliques S7 belongs to. He is part of the only 50+ (and 100+) member clique, and a part of over 60% of all the other clique categories. These factors all make S7 a central figure in the class. From the LOG files of the course, we were able to see that student S7 had completed all 15 lessons, had visited all 15 lesson forums and read the threads, and had contributed his/her own postings to 14 out of the 15 lessons. To see the relevancy of what student S7 talked about, we analyzed his discussion board contributions using the Topic Relation Analysis (TRA) method. In TRA (Laghos, 2005) the student s postings are grouped into the following categories: A - Course Material related A1 - Related to current Lesson (eg questions and answers and correcting peers mistakes) A2 - Related to course but not current lesson (eg conversations that were posted in the wrong lesson s forum) B - Course Website/Technical Related (eg. problems listening to audio, or accessing specific parts of the site) C - Not related to course C1 - Peer socializing (eg student introductions, discussions about football games, making new friends etc) C2 other (eg spam and advertisements). Table 3. S7 clique analysis. Cliques of Total Of which S7 is part of % Threads Participated In Table 4. Student S7 s TRA results. Messages Posted A1 A2 B C1 C2 A1 A2 B C1 C Table 5. S7 s contributions. Threads Messages # % # % A B C Total Table 4 shows S7 s TRA results while it is obvious from table 5 that the majority (over 60%) of S7 s postings had to do with the course in question. Also, it should be noted that in Lesson 1, S7 contributed many more messages in the Peer socializing category, than in the rest of the lessons, as a normal teacher would when first meeting the students in a face-to-face classroom. We remind that contribution to the discussion board in this course is completely voluntary and thus S7 participated on his/her own will. Not only did he/she send the most messages, but was also the one who received the most messages. This is exactly how a teacher would interact with the students in a classroom, where the vast majority of communication that takes places is directed as: teacher-students, students-teacher, but less as students-students, as was the case in LGO with student S7. Thus we conclude that in this student-centred e-learning community, one of the students did take on the role of the teacher. 4. CONCLUSION

9 In this paper we used a number of methods (SNA, TRA, Log analysis) to study an online student centred e- Learning community. The case study was a course for learning the Greek language and did not have a teacher to supervise the process. We performed an in-depth analysis on the network, and followed its evolution through the 15 lessons of the course. We were able to identify roles of students with similar interaction patterns as well as to obtain centrality, clique, and other measurements. Our results showed that one student had communication patterns that resembled that of a teacher in classroom settings. Future directions include the same type of analysis for the remaining identified role groups R2, R3 and R4 in order to create their communication personas as well. Using evolutionary SNA, TRA, log analysis and questionnaires we will be investigating what causes the changes in the network, and why certain students take on certain roles in the e-learning community. REFERENCES Aviv, R. et al, Cohesion and Roles: Network Analysis of CSCL Communities. IEEE ICALT Bock, R.D., and Husain, S.Z., An adaptation of Holzinger's B-coefficients for the analysis of sociometric data. In Sociometry, 13, pp Burt, R.S Structural Holes: The social structure of competition. Harvard University Press. Cambridge. Cyram Netminer for Windows. Dekker, A.H A Category-Theoretic Approach to Social Network Analysis. Proceedings of Computing: The Australasian Theory Symposium (CATS), Melbourne, Australia De Nooy, W. et al, Exploratory social network analysis with Pajek. Cambridge University Press. New York. Dougiamas, M Moodle: Open-source software for producing internet-based courses. Everett M. G., and Borgatti S. P Two algorithms for computing regular equivalence. Social Networks 15, Floyd R. W Algorithm 97 (SHORTEST PATH). Communications of the ACM, 5(6):345 Freeman, L.C The gatekeeper, pair-dependency, and structural centrality. Quality and Quantity, 14, Freeman, L.C Centrality in Social Networks: Conceptual clarification. Social Networks 1, Harary, F Graph Theory. Addison-Wesley. Reading, MA. Knoke, D. & Kuklinski, J.H Network Analysis. Sage University Paper Series on Quantitative Applications in Social Sciences. Beverly Hills and London. Krebs, V An Introduction to Social Network Analysis. Retrieved November 9, 2004 from Kypros-Net Inc The World of Cyprus. Laghos, A FESNeL: A Methodological Framework for Assessing the Evolutionary Structure of Social Networks in e-learning. Proceedings of the 11th Biennial JURE/EARLI Conference, Nicosia, Cyprus. Lorrain, F and White, H. C Structural equivalence of individuals in social networks. Journal of Mathematical Sociology, 1, Preece, J Online Communities: Designing Usability, Supporting Sociability. John Wiley and Sons. Chichester, UK Preece, J. et al, Interaction Design: Beyond Human-Computer Interaction. John Wiley & Sons. New York, NY Rheingold, H The Virtual Community: Homesteading on the Electonic Frontier. Addison-Wesley. Reading Rice, R. E. et al, Individual and network influences on the adoption and perceived outcomes of electronic messaging. Social Networks, 12, Rice, R Network analysis and computer mediated communication systems. In S. W. J. Galaskiewkz (ed.), Advances in Social Network Analysis. Sage. Newbury Park, CA. Roberts Jr, J.M Simple methods for simulating sociomatrices with given marginal totals. Social Networks 22, Scott, J Social Network Analysis: A handbook. Second edition. Sage. London. Wasserman, S., and Faust, K Social Network Analysis: Methods and Applications. Cambridge. Wellman, B An Electronic Group is Virtually a Social Nework. In Kiesler, S. (Ed.), Culture of the Internet. Lawrence Erlbaum Assoc Hillside, New Jersey. Winship, C., and Mandel, M Roles and Positions: A critique and extension of the blockmodeling approach. In Leinhardt, S. (ed.), Sociological Methodology. Jossey-Bass. San Francisco. Zaphiris, P., Zacharia, G Design Methodology of an Online Greek Language Course. Ext. Abstracts CHI 2001, ACM Press.