Using Machine Learning to Predict Trouble During Collaborative Learning

Our goal is to build and evaluate a web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent electronic agent that will aid them in their learning. The agent will follow the discussion and interact with the participants when it detects learning trouble. In order to recognize problems in the dialogue, we investigated conversational elements that can be utilized as predictors for effective and ineffective interaction between human students. In this paper we discuss our representation of participant dialogue and the statistical models we are using to determine the effectiveness of group interaction.