"Learning-in-action" depends on interactions with learning content, peers and real world problems. However, effective learning-in-action also depends on the extent to which students are "active-in-thinking," making meaning of their learning experience. A critical component of any technology to support active thinking is the ability to ascertain whether (or to what extent) students have succeeded in internalizing the disciplinary strategies, norms of thinking, discourse practices and habits of mind that characterize deep understanding in a domain. This presents what we call a dilemma of "modeling-in-context": teachers routinely analyze this kind of thinking for small numbers of students in activities they create or customize for the needs of their students; however, doing so at scale and in real-time requires some automated processes for modeling student work. Current techniques for developing models that reflect specific pedagogical activities and learning objectives that a teacher might create require either more expertise or more time than teachers have. In this paper, we examine a theoretical approach to addressing the problem of modeling active thinking in its pedagogical context that uses teacher-created rubrics to generate models of student work. The results of this examination show how appropriately constructed learning technologies can enable teachers to develop custom automated rubrics for modeling active thinking and meaning-making from the records of students' dialogic work.