Massive open online courses (MOOCs) have demonstrated growing popularity and rapid development in recent years. Discussion forums have become crucial components for students and instructors to widely exchange ideas and propagate knowledge. It is important to recommend helpful information from forums to students for the benefit of the learning process. However, students or instructors update discussion forums very often, and the student preferences over forum contents shift rapidly as a MOOC progresses. So, MOOC forum recommendations need to be adaptive to these evolving forum contents and drifting student interests. These frequent changes pose a challenge to most standard recommendation methods as they have difficulty adapting to new and drifting observations. We formalize the discussion forum recommendation problem as a sequence prediction problem. Then we compare different methods, including a new method called context tree (CT), which can be effectively applied to online sequential recommendation tasks. The results show that the CT recommender performs better than other methods for MOOCs forum recommendation task. We analyze the reasons for this and demonstrate that it is because of better adaptation to changes in the domain. This highlights the importance of considering the adaptation aspect when building recommender system with drifting preferences, as well as using machine learning in general. [For the full proceedings, see ED596512.]