On efficiently tracking turn-taking patterns in a CSCL environment using Lempel-Ziv complexity analysis

A complexity-based analysis of the turn-taking sequences produced during peers' computer-mediated collaboration is presented in this paper. The collaborative contributions monitored by the system, namely Lin2k, are mapped to turn-taking sequences, which, in turn, are transformed to symbol-sequences and analyzed for pattern extraction. The use of a normalized complexity measure when applied to collaborative data from the field of environmental engineering reveals peers' tendencies to more complex turn-taking patterns when they receive appropriate feedback from the Lin2k. The efficiency of the proposed complexity analysis in pattern identification and its simplicity in implementation makes it a useful tool for effective tracking of temporal changes in collaborative activity.