Educational Question Routing in Online Student Communities

Abstract

Students' performance in Massive Open Online Courses (MOOCs) is enhanced by high quality discussion forums or recently emerging educational Community Question Answering (CQA) systems. Nevertheless, only a small number of students answer questions asked by their peers. This results in instructor overload, and many unanswered questions. To increase students' participation, we present an approach for recommendation of new questions to students who are likely to provide answers. Existing approaches to such question routing proposed for non-educational CQA systems tend to rely on a few experts, what is not applicable in educational domain where it is important to involve all kinds of students. In tackling this novel educational question routing problem, our method (1) goes beyond previous question-answering data as it incorporates additional non-QA data from the course (to improve prediction accuracy and to involve more of the student community) and (2) applies constraints on users' workload (to prevent user overloading). We use an ensemble classifier for predicting students' willingness to answer a question, as well as students' expertise for answering. We conducted an online evaluation of the proposed method using an A/B experiment in our CQA system deployed in edX MOOC. The proposed method outperformed a baseline method (non-educational question routing enhanced with workload restriction) by improving recommendation accuracy, keeping more community members active, and increasing an average number of their contributions.