Researchers have been interested in classifying massive open online course (MOOC) students based on their learning behaviors. However, less attention has been paid to the cognitive attributes associated with various learning behaviors. In this study, we propose a conceptual model that links MOOC students' observable learning behaviors to their latent attributes (i.e., individual learning versus interactive learning). Using students' behavior data from a MOOC, we performed a cognitive diagnostic analysis to identify the students' learning profiles and to determine how these profiles related to their course achievement. We found that a large portion of the students performed individual learning whereas only a very small portion of them overtly performed interactive learning. In addition, the students who performed interactive learning were more likely to pass the course with distinction than the students who did not show this attribute. The results of this study have important implications for improving students' learning in MOOCs. Further, the study provides a good demonstration of how to use clickstream process data for psychometric analysis.