Our knowledge of a topic such as mathematics is reliant upon the hierarchies of chunks we build in our memories. The time course of knowledge-based tasks, such as the transcription of algebraic formulas, provides rich signals that reflect the structure of the chunk hierarchies being processed. By building Gaussian Mixture Models, this paper provides evidence that decomposing the overall dis-tribution of pauses between actions in a sequential task can give meaningful characterizations of the structure of the chunk hierarchy. We also examine whether individual competence in mathematics can be measured using a metric derived from the models.