Online learning systems allow learners to freely access learning contents and record their interactions throughout their engagement with the content. By using data mining techniques on the student log data of those systems, it is possible to examine learning behavior and reveal navigation patterns through learning contents. This study was aimed at investigating how learners' characteristics affect their online learning behavior by using sequential pattern mining and clustering techniques. Participants were 74 undergraduate students from a public university, whose interactions with four different kinds of learning contents in an online learning system were collected. Results showed significant differences in sequential patterns of learners with different cognitive characteristics.