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 necessary expert knowledge is application and domain specific, the entire model development process must be repeated for each new application. In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised machine learning to reduce the costs of building student models, and facilitate transferability. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data). Despite limitations due to the size of our datasets, we provide initial evidence that the framework can automatically identify meaningful student interaction behaviors and can be used to build user models for the online classification of new student behaviors online. We also show framework transferability across applications and data types.