Data cracker: developing a visual game analytic tool for analyzing online gameplay
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
| Hi-index | 0.00 |
Knowledge flow in interdisciplinary teams has become of particular interest as research and alliances cross traditional disciplinary boundaries, and as computing is applied in any domain. The problem is how to share and/or pool knowledge toward a common goal among people with diverse backgrounds. To understand the needs of such groups, it is important to find out what knowledge and learning flows form the basis of their relationships. To explore this, social network data were collected from members of three interdisciplinary teams about what they learned from their closest 5-8 co-workers, and what co-workers learned from them. Results show exchange of factual knowledge to be only one of a number of learning relations that support the teams. Other important relations include learning the process of doing something, information about methods, engaging jointly in research, learning about technology, generation of new ideas, socialization into the profession, access to a network of contacts, and administration work. Distributions of these relations are examined.