  Problem solving in physics and mathematics have been characterized in terms
of five phases by Schonfeld and these have previously been used to describe
also online and blended behavior. We argue that expanding the use of server
logs to make detailed categorizations of student actions can help increase
knowledge about how students solve problems. We present a novel approach for
analyzing server logs that relies on network analysis and principal component
analysis. We use the approach to analyze student interactions with an online
textbook that features physics problems. We find five 'components of behavioral
structure': Complexity, Linear Length, Navigation, Mutuality, and Erraticism.
Further, we find that problem solving sessions can be divided into three
over-arching groups that differ in their Complexity and further into ten
clusters that also differ on the other components. Analyzing typical sessions
in each cluster, we find ten different behavioral structures,which we describe
in terms of Schonfeld's phases. We suggest that further research integrates
this approach with other methodological approaches to get a fuller picture of
how learning strategies are employed by students in settings with online
features.
