Despite several advantages of online education, lack of effective student-instructor interaction, especially when students need timely help, poses significant pedagogical challenges. Motivated by this, we address the problems of automatically identifying posts that express confusion or urgency from Massive Open Online Course (MOOC) forums. To this end, we first investigate the extent to which the tasks of confusion detection and urgency detection are correlated so as to explore the possibility of utilizing a multitasking set-up. We then propose two LSTM-based [Long Short Term Memory-based] multitask learning frameworks to leverage shared information and transfer knowledge across these related tasks. Our experiments demonstrate that the approaches improve over single-task models. Our best-performing model is especially useful in identifying posts that express both confusion and urgency, which can be of particular relevance for forum curation. [For the full proceedings, see ED607784.]