Microblogging is a popular technology in social networking applications that lets users publish online short text messages (e.g., less than 200 characters) in real time via the web, SMS, instant messaging clients, etc. Microblogging can be an effective tool in the classroom and has lately gained notable interest from the education community. This paper proposes a novel application of text categorization for two types of microblogging questions asked in a classroom, namely relevant (i.e., questions that the teacher wants to address in the class) and irrelevant questions. Empirical results and analysis show that using personalization together with question text leads to better categorization accuracy than using question text alone. It is also beneficial to utilize the correlation between questions and available lecture materials as well as the correlation between questions asked in a lecture. Furthermore, empirical results also show that the elimination of stopwords leads to better correlation estimation between questions and leads to better categorization accuracy. On the other hand, incorporating students' votes on the questions does not improve categorization accuracy, although a similar feature has been shown to be effective in community question answering environments for assessing question quality. (Contains 5 tables and 1 figure.)