Concept mapping is a technique that represents knowledge in graphs. It has been widely adopted in science education and cognitive psychology to aid learning and assessment. To realize the sequential manner in which students develop concept maps, most research relies upon human-dependent, qualitative approaches. This article proposes a method for sequential pattern analysis, inspired by sequential pattern mining algorithms generally applied to commercial forecast and decision supports. The method can be programmed for automatic execution and thus reasonably fast, yielding reproducible results. To validate the proposed method, 187 college students were recruited to create respective concept maps on a computerized concept mapping system. While the concept mapping data was analyzed by the sequential pattern analysis method, it was found that the mapping sequences used by students that created superior concept maps were similar and had a pattern in which propositions were formed in a temporal order from more inclusive to less inclusive. Conversely, no similarity was found in the concept mapping sequences by those who created inferior concept maps. The findings support theoretical expectations about concept mapping and are consistent with qualitative evidence based on student self-reports. (Contains 6 figures and 1 table.)