INVESTIGATING STUDENT ROLES IN ONLINE STUDENT-CENTERED LEARNING

In this study we used the technique of Social Network Analysis (SNA) to investigate the roles students take on in an online student-centered learning environment, and more specifically in an online Computer Aided Language Learning (CALL) course. SNA is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities (Krebs, 2004). We studied the interactions and communications of the students in the discussion boards of the language course and carried out Equivalence Analysis. Equivalence of the network members shows when two actors have similar patterns of relations. In other words, students with similar communication behaviors are grouped together. Being able to define, theorize about, and analyze data in terms of equivalence is important because we want to be able to make generalizations about social behaviour and social structure (Hanneman, 2001). Two nodes are said to be structurally equivalent if they have identical ties with themselves, each other and all other vertices. The aim of equivalence is to classify actors with similar roles into role groups by embedding the actors in a certain role space, identifying clusters of students and then carrying out subsequent cluster analysis to identify their roles. After the role types were identified we used a number of other methods in order to get more details and characteristics of the role groups. These methods included the Topic Relation Analysis (TRA) which is a content analysis tool used to group the students messages into conversation categories, the Attitudes Towards Thinking and Learning Survey (ATTLS) which is used to measure the quality of discourse within the course and the extent which a person is a 'connected knower' (CK) or a 'separate knower' (SK), and the Collectivist On-Line Learning Survey (COLLES) which measures students’ perceptions and preferences and was designed to help teachers assess, from a social constructivist perspective, the quality of their online learning environment (Taylor and Maor, 2000). Our findings show that four main roles types (R1-R4) where identified and one of the students in particular (R1) had communication patterns that resembled those of a teacher in classroom settings. The student in R2 would provide his own lectures notes and was connected with a large number of his peer students who depended on him for this material, he was part of a high number of cliques and his contributions in the discussion boards were mainly on course related material. The R3 students interacted with a small number of their peers but at a higher frequency, thus working more in small teams. In addition, the majority of their usage of the discussion boards was about the course material and helping out their peers. Finally, the students in R4 made connections with a large number of their peers, their discussions were mainly on social topics and not course related, they prefer to learn on their own and were mainly using the discussion boards to make friends and socialize with their peers.

[1]  John Scott Social Network Analysis , 1988 .

[2]  P. Taylor,et al.  Establishing open and critical discourses in the science classroom: Reflecting on initial difficulties , 1998 .

[3]  Vladimir Batagelj,et al.  Exploratory Social Network Analysis with Pajek , 2005 .

[4]  Kimberly A. Fredericks,et al.  An introduction to social network analysis , 2005 .

[5]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[6]  Ronald E. Rice,et al.  Network Analysis and Computer-Mediated Communication Systems , 1994 .

[7]  Gilad Ravid,et al.  Cohesion and roles: network analysis of CSCL communities , 2003, Proceedings 3rd IEEE International Conference on Advanced Technologies.

[8]  K. Galotti,et al.  A New Way of Assessing Ways of Knowing: The Attitudes Toward Thinking and Learning Survey (ATTLS) , 1999 .

[9]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[10]  M. C. O'Connor,et al.  Can We Trace the "Efficacy of Social Constructivism"? , 1998 .

[11]  Dorit Maor,et al.  Assessing the efficacy of online teaching with the Constructivist online learning environment survey , 2000 .

[12]  R. Hanneman Introduction to Social Network Methods , 2001 .

[13]  M. Belenky,et al.  Women's ways of knowing : the development of self, voice, and mind , 1988 .

[14]  R. Darrell Bock,et al.  An Adaptation of Holzinger's B-Coefficients for the Analysis of Sociometric Data , 1950 .

[15]  Peter Charles Taylor,et al.  Moodle: Using Learning Communities to Create an Open Source Course Management System , 2003 .

[16]  Anthony H. Dekker A Category-Theoretic Approach to Social Network Analysis , 2002, Electron. Notes Theor. Comput. Sci..

[17]  D. Hilton,et al.  Peer support: a theoretical perspective. , 2001, Psychiatric rehabilitation journal.

[18]  Charalambos Vrasidas CONSTRUCTIVISM VERSUS OBJECTIVISM: IMPLICATIONS FOR INTERACTION, COURSE DESIGN, AND EVALUATION IN DISTANCE EDUCATION. , 2000 .

[19]  Jenny Preece,et al.  Online Communities: Designing Usability and Supporting Sociability , 2000 .

[20]  Petri Nokelainen,et al.  The role of the learning platform in student-centred e-learning , 2004, IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings..

[21]  A. Sfard On Two Metaphors for Learning and the Dangers of Choosing Just One , 1998 .

[22]  Andrew Laghos,et al.  SOCIOLOGY OF STUDENT-CENTRED E-LEARNING COMMUNITIES: A NETWORK ANALYSIS , 2007 .

[23]  Matthew W. Lewis,et al.  Self-Explonations: How Students Study and Use Examples in Learning to Solve Problems , 1989, Cogn. Sci..

[24]  P. Berger,et al.  The Social Construction of Reality , 1966 .

[25]  John Sumner,et al.  Peer-to-peer eLearning and the team effect on course completion , 2002, International Conference on Computers in Education, 2002. Proceedings..

[26]  Etienne Wenger,et al.  Communities of Practice: Learning, Meaning, and Identity , 1998 .