In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. 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).…

International student admissions in universities can be facilitated by multivariate statistical procedures. On the basis of existing data on student characteristics, predictions can be made regarding performance of applicants. These predictions consider personal variables, knowledge of English, and various indicators of academic performance.…