Combining Unsupervised and Supervised Machine Learning to Build User Models for Intelligent Learning Environments

Traditional approaches to developing user models, especially for computer-based learning environments, are notoriously difficult and time-consuming because they rely heavily on expert-elicited knowledge about the target application and domain. Furthermore, because the expert-elicited knowledge used in the user model is application and domain specific, the entire model development process must be repeated for each new application. In this thesis, we outline a data-based user modeling framework that uses both unsupervised and supervised machine learning in order to reduce the development costs of building user models, and facilitate transferability. We apply the framework to build user models of student interaction with two different learning environments (the CIspace Constraint Satisfaction Problem Applet for demonstrating an Artificial Intelligence algorithm, and the Adaptive Coach for Exploration for mathematical functions), and using two different data sources (logged interface and eye-tracking data). Although these two experiments are limited by the fact that we do not hav