Computational analysis of knowledge sharing in collaborative distance learning

The rapid advance of distance learning and networking technology has enabled universities and corporations to reach out and educate students across lime and space barriers. This technology supports structured, on-line learning activities, and provides facilities for assessment and collaboration. Structured collaboration, in the classroom, has proven itself a successful and uniquely powerful learning method. Online collaborative learners, however, do not enjoy the same benefits as face-to-face learners because the technology provides no guidance or direction during online discussion sessions. Integrating intelligent facilitation agents into collaborative distance learning environments may help bring the benefits of the supportive classroom closer to distance learners. In this dissertation, I describe a new approach to analyzing and supporting online peer interaction. The approach applies Hidden Markov Models, and Multidimensional Scaling with a threshold-based clustering method, to analyze and assess sequences of coded on-line student interaction. These analysis techniques were used to train a system to dynamically recognize when and why students may be experiencing breakdowns while sharing knowledge and learning from each other. I focus on knowledge sharing interaction because students bring a great deal of specialized knowledge and experiences to the group, and how they share and assimilate this knowledge shapes the collaboration and learning processes. The results of this research could be used to dynamically inform and assist an intelligent instructional agent in facilitating knowledge sharing interaction, and helping to improve the quality of online learning interaction.