Massive open online courses (MOOCs) have been touted as an effective way to make higher education accessible for free or for only a small fee, thus addressing the problem of unequal access and providing new opportunities to young people in middle and low income groups. However, many critiques of MOOCs have indicated that low completion rates are a major concern. Using a latent class analysis (LCA), a more advanced methodology to identify latent subgroups, this study examined the heterogeneity of learners' behavioral patterns in a MOOC, categorized them into distinctive subgroups, and ultimately determined the optimal number of latent subgroups in a MOOC. The five subgroups identified in this study were: "completing" (6.6%); "disengaging" (4.8%); "auditing" (4.6%); "sampling" (21.1%); and "enrolling" (62.8%). Results indicated this was the optimal number of subgroups. Given the characteristics of the three at-risk subgroups (disengaging, sampling, and enrolling), tailored instructional strategies and interventions to improve behavioral engagement are discussed.