At a time when public schools are making larger investments in hardware and software, and colleges and universities are increasingly turning to distance education technology to reach a broader customer base, it is critical to maximize the effectiveness of technology for learning. This project will improve understanding of how collaborative learning is facilitated by computer software with which learners construct and manipulate visual representations of their emerging knowledge. "Representational bias" refers to how these software environments facilitate the expression and inspection of different kinds of information.
In this project, students will use four software environments for recording and organizing hypotheses and empirical observations while investigating a recent scientific problem. The software environments will each provide users with a different representational device, namely threaded discussion lists, nested boxes, graphs, and matrices. Students' talk, gestures, and use of the software will be transcribed and analyzed to identify the concepts that students use and the types of knowledge that student seek and discuss, testing specific predictions concerning how the representational bias of each of the four representational devices affect learning processes. Learning outcomes will also be assessed. Studies will be undertaken with both secondary school students working side by side in front of a computer and with college students collaborating remotely in a distance learning paradigm.
The research will provide a better theoretical understanding of the role of representational bias in guiding collaborative learning and problem solving processes. Such an understanding will inform the design of more effective collaborative learning and distance learning environments, and will also have applications to the design of representational tools for a variety of other knowledge networking applications, such as collaborations between scientists and other practitioners.
