Conversations in collaborative problem-solving activities can be used to probe the collaboration skills of the team members. Annotating the conversations into different collaboration skills by human raters is laborious and time consuming. In this report, we report our work on developing an automated annotation system, CPS-rater, for conversational data from collaborative activities. The linear chain conditional random field method is used to model the sequential dependencies between the turns of the conversations, and the resulting automated annotation system outperforms those systems that do not model the sequential dependency.