Students often struggle with solving mechanism problems in organic chemistry courses. They frequently focus on surface features, have difficulty attributing meaning to symbols, and do not recognize tasks that are different from the exact tasks practiced. To be more successful, students need to be able to extract salient features, map similarities to problems seen previously, and extrapolate while solving problems. In short, students must be able to recognize and generate abstractions. To help students in learning to solve problems, we need a better understanding of the nature of students' capacity for abstraction. Building upon an exploratory study (Sevian H., Bernholt S., Szteinberg G. A., Auguste S. and Pérez L. C., (2015), "Use of representation mapping to capture abstraction in problem solving in different courses in chemistry," "Chem. Educ. Res. Pract.," 16(3), 429-446), we applied the representation mapping model of Hahn and Chater (1998a) to characterize the abstraction employed by students while solving mechanistic problems in organic chemistry, and to measure students' growth in abstraction capacity across a semester. This model operationalizes abstraction by considering (a) the ways in which students match existing knowledge to new instances (abstracting) and (b) the level of abstractness of students' representations. We describe characteristic indicators of abstracting and abstractness. Trends were observable in the abstraction present in the reasoning of successful and unsuccessful problem solvers. Students who proposed plausible solutions used both strict or partial matching, but students who proposed implausible solutions tended to use strict matching. Students who proposed plausible solutions utilized higher levels of abstractness. This indicates that flexibility in abstraction processes may be important to successfully solve problems. The findings have implications for developing instructors' assessment practices in ways that build students' abstraction capacity.