Reuven Aviv

Pedagogical Communication Patterns in Collaborative Telelearning

Collaborative learning is an indispensable part of Telelearning Distance Education courses. It is naturally implemented via such activities as data collection, projects, and field studies. The worldwide revolution in the telecommunication environment that has been gathering momentum in recent years is bringing radical changes in the collaboration channels. To prepare for these changes, it is vital to identify and solve a wide range of educational and organizational problems involving the embedding of learning and teaching procedures in a telecommunication rich environment. This article is concerned with the pedagogical communication patterns between students and tutors. The result of evaluating students behavior in a set of telelearning Computer Science courses show that they differentiate between the personal dimension of communicating with others and the group dimension which enable true collaborative work. The former depends mainly on the extent to which they need help, whereas the later depends on the design of the group collaboration and the creative leadership of the class teacher or the tutor.

Cohesion and roles: network analysis of CSCL communities

We provide empirical support for the assertions that high level of knowledge construction is associated with structured design and that knowledge construction is associated with cohesion and equivalence network structures. We built and analyzed two CSCL communities - one structured the other nonstructured. The levels of learning processes were measured by content analysis. The social capital structure of the communities was analyzed by social network analysis. The analysis revealed that the structured community developed social capital, encoded by a mesh of interlinked cliques, and that participants undertook bridging and triggering roles, and exhibited high levels of constructing knowledge. The tutor (guide) remained on the side. The nonstructured community did not construct knowledge, cohesion was dull, and participants did not undertake any essential roles.

Mechanisms and architectures of online learning communities

Online communities are described in terms of collections of virtual neighborhoods, each of which is a subset of interdependent members. The significant virtual neighborhoods are revealed by fitting parametric Markov field models (p*) to the response relations of the communities. The underlying theoretical mechanisms are then deduced by matching the revealed virtual neighborhoods with the predictions of network emergence theories. We demonstrate that the underlying mechanisms are related to specific design features of the communities. This method can be extended to other relations in online communities and to longitudinal analysis, and applied to real-time monitoring of online communications.