Formal methods are used successfully in high-assurance software, but they require rigorous mathematical and logical training that practitioners often lack. As such, integrating formal methods into software has been associated with numerous challenges. While educators have placed emphasis on formalisms in undergraduate theory courses, such courses often struggle with poor student outcomes and satisfaction. In this paper, we present a controlled eye-tracking human study (n=34) investigating the problem-solving strategies employed by students with different levels of incoming preparation (as assessed by theory coursework taken and pre-screening performance on a proof comprehension task), and how educators can better prepare low-outcome students for the rigorous logical reasoning that is a core part of formal methods in software engineering. We find that incoming preparation is not a good predictor of student outcomes for formalism comprehension tasks, and that student self-reports are not accurate at identifying factors associated with high outcomes for such tasks. Instead, and importantly, we find that differences in outcomes can be attributed to performance for proofs by induction and recursive algorithms, and that better-performing students exhibit significantly more attention switching behaviors, a result that has several implications for pedagogy in terms of the design of teaching materials. Our results suggest the need for a substantial pedagogical intervention in core theory courses to better align student outcomes with the objectives of mastery and retaining the material, and thus bettering preparing students for high-assurance software engineering. This artifact makes publicly available the de-identified eye-tracking and facial behavior analysis data that we collected in our controlled study of cognition of proofs about algorithms. We also include our Python scripts (as several Jupyter notebooks) used for the statistical analyses of the collected data.