Seven Factors that Make Learning Successful in Networked Collaboration

This paper examines seven strategic factors that seem to make learning successful in networked collaboration. Through our literature review, it was found that there are at least seven strategic factors: deep thinking, more interaction, cognitive conflict resolution, adaptation over time, constructive use of technology, task coordination between media, and asynchrousness management. It is concluded that the effectiveness of learning and problem solving in networked collaboration is determined by students’ use of those factors in networked collaboration. One of the most famous collaborations in history was between Herbert Simon, Allen Newell, and J. C. Shaw who worked together over the phone. Simon was at Carnegie Institute of Technology in Pittsburgh and Newell and Shaw were at RAND in California. McCorduck (1979) wrote: “... Simon and Cliff Shaw seldom saw or spoke to each other. Newell carried out the middleman’s role, mostly by longdistance telephone between Pittsburgh and Santa Monica” (p. 139). However, they were one of the most effective teams in spite of the difficulties working at a distance. In present day, we work together with our colleagues using the help of the state-of-art technology called computer-mediated communication (CMC). Once you sit at your desk, you can see whether your partners are available, busy or even happy using an instant messenger. You can communicate through email or video-mediated conference with your colleagues who are within 10 steps or in another continent. However, the state-of-art networked collaboration technology seems not straightforward to result in better performance although barriers set by time and space have been removed. So, what factors make networked collaboration effective or ineffective for learning and problem solving? That is the topic that this review intends to investigate. Dominant theories in CMC such as social presence theory (Short, Williams, & Christie, 1976), media richness theory (Daft & Lengel, 1986), task-media fitness theory (McGrath & Hollingshead, 1994), etc. argue that the bandwidth or capacities of transmitting information in each communication medium determine the effectiveness of networked collaboration in learning and problem solving. We call these theories the face-to-face (FTF) superiority theories since they qualify the effectiveness of networked collaboration through CMC in terms of the amount of information each CMC medium transmits. According to the FTF superiority theories, networked collaboration filters out various information such as social cues. Hence, networked collaboration cannot be as effective as FTF with possible exceptions where networked collaboration is task-oriented (Sproull & Kiesler, 1991) and where a task does not require more than a given network bandwidth (McGrath & Hollingshead, 1994). However, empirical studies tend not to find supportive evidence when the quantity and/or quality of performance instead of self-report were taken into account, and when bandwidth as an independent was manipulated (e.g. Dennis & Kinney, 1998). Also many studies show that networked collaboration is as effective as FTF (e.g. Vera, Kvan, West, & Lai, 1998). Thus, it seems that the impact of bandwidth is not a critical factor, but rather deactivated by another factor, which we assume the role of active agents in the processes of networked collaboration. In contrast to the FTF superiority theories, cognitive perspectives try to account for learning and problem solving in networked collaboration in terms of how cognitive agents adapt and behave because “learning rests on the learning skills that the students themselves bring to bear as they learn” (Chi, Lewis, Reimann, & Glaser, 1989, p. 146). Thus, learners are expected to be involved in constructive processes where they construct knowledge, skills, and ways of working together regardless of whether they are in adverse or auspicious situations. Hence, in a low-bandwidth network, they are still expected to overcome given difficulties by maximizing the effectiveness of knowledge, strategies or skills. For example, Cho, Schunn, and Lesgold (2002) compared successful dyads with unsuccessful dyads in networked collaboration. Participants were asked to work together to understand texts while interacting with peers through a text-based generic chat interface. It was shown that in general the unsuccessful dyads tend to be involved in less effective learning processes like checking each other’s answer or rephrasing, while the successful groups tended to be involved in more effective processes like explanation or elaboration. In the face of explicit errors or misunderstandings, the successful groups explored their understandings in more depth by implementing elaboration. However, the unsuccessful groups didn’t explore what caused their comprehension failure and tended to keep their errors. Therefore, the prime consideration of this paper is to reveal what processes make networked collaboration successful for effective learning and problem solving.