Adaptive educational software is likely to better support broader and more diverse sets of learners by considering more comprehensive views (or models) of such learners. For example, recent work proposed making inferences about "non-math" factors like reading comprehension while students used adaptive software for mathematics to better support and adapt to learners. We build on this proposed approach to more comprehensive learning modeling by providing an empirical basis for making inferences about students' reading ability from their performance on activities in adaptive software for mathematics. We lay out an approach to predicting middle school students' reading ability using their performance on activities within Carnegie Learning's MATHia, a widely used intelligent tutoring system for mathematics. We focus on how performance in an early, introductory activity as an especially powerful place to consider instruction-embedded assessment of non-math factors like reading comprehension to guide adaptation based on factors like reading ability. We close by discussing opportunities to extend this work by focusing on particular knowledge components or skills tracked by MATHia that may provide important "levers" for driving adaptation based on students' reading ability while they learn and practice mathematics. [This paper was published in: "LAK23: 13th International Learning Analytics and Knowledge Conference," ACM, 2023.]