Enhancing user support in open problem solving environments through Bayesian Network inference techniques.

During the last years, development of open learning environments that support effectively their users has been a challenge for the research community of educational technologies. The open interactive nature of these environments results in users experiencing difficulties in coping with the plethora of available functions, especially during their initial efforts to use the system. In addition, -from the tutors' perspective- the problem solving strategies of the students are often particularly difficult to identify. In this paper, we argue that such problems could be tackled using machine learning techniques such as Bayesian Networks. We show how we can take advantage of log files obtained during field studies to build an adaptive help system providing the most useful support to the student, according to the state of interaction. On the other hand, we attempt to support the tutor, by automating the process of diagnosing students' problem solving strategies using Bayesian Networks. The presented approaches are discussed through examples of two prototypes that have been developed and corresponding evaluation studies. These studies have shown that the proposed approach can effectively support the tasks of students and tutors in such open learning environments. [ABSTRACT FROM AUTHOR]