Inferring frequently asked questions from student question answering forums

Abstract

Question answering forums in online learning environments provide a valuable opportunity to gain insights as to what students are asking. Understanding frequently asked questions and topics on which questions are asked can help instructors in focusing on specific areas in the course content and correct students’ confusions or misconceptions. An underlying task in inferring frequently asked questions is to identify similar questions based on their content. In this work, we use hierarchical agglomerative clustering that exploits similarities between words and their distributed representations, reflecting both lexical and semantic similarity of questions. We empirically evaluate our results on real world labeled dataset to demonstrate the effectiveness of the method. In addition, we report the results of inferring frequently asked questions from discussion forums of online learning environment providing lectures to middle school and high school students.