In the discussion forums of massive open online courses (MOOCs), cognitive processing (e.g., insight, certain) is considered an essential factor that can affect learners' learning outcomes, but the relationship between them has not been thoroughly investigated. Especially the dynamic nature of cognitive processing is still a significant research gap. In this study, we proposed an unsupervised topic model named Temporal Cognitive Topic Model (TCTM) to automatically classify cognitive processes and obtain the conditional probability with topics over time. The results indicated that completers had more active and timely cognitive engagement as time went on and tended to use certain cognitive words to discuss the topics related to the examination and certificates, which showed that they had explicit learning goals and plans. Non-completers often used exclusive cognitive words to discuss some off-task content that pointed out a distractive learning process. Using the model, teachers can capture learners' dynamic cognitive states and associated topics to improve teaching methods and increase course completion rates.