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).…

A broad range of well-structured problems--embracing forms of diagnosis, catalog selection, and skeletal planning--are solved in expert computer systems by the method of heuristic classification. These programs have a characteristic inference structure that systematically relates data to a pre-enumerated set of solutions by abstraction, heuristic…

The current state of the art on the educational use of microcomputers was explored through a review of the literature, observations, and interviews with teachers and practitioners. A working taxonomy to characterize typical computer-augmented learning environments (CALEs) was developed which consisted of environmental elements, activity segments,…