Placing students in a group and assigning them a task does not guarantee that the students will engage in effective collaborative learning behavior. The collaborative learning model described in this paper identifies the specific characteristics exhibited by effective collaborative learning teams, and based on these characteristics, suggests strategies for promoting effective peer interaction. The model is designed to help an intelligent collaborative learning system recognize and target group interaction problem areas. We describe the empirical evaluation of two collaborative learning tools that automate the analysis of peer interaction and activity. Results from our study confirm that effective learning teams are comprised of active participants who demand explanations and justification from their peers. The results also suggest that structured, high-level knowledge of student conversation and activity appears to be sufficient for automating the assessment of group interaction, furthering the possibility of an intelligent collaborative learning system that can support and enhance the group learning process.