Improving Student Forum Responsiveness: Detecting Duplicate Questions in Educational Forums

Student forums are important for student engagement and learning in university courses but require high staff resources to moderate and answer questions. In introductory courses, the content can remain almost unchanged each year, so the questions asked in the course forums do not see a lot of variety over different iterations, which provides an opportunity for automation. This paper compiles a dataset of forum threads and meta-information of the participants from the Web Design and Development course at the Australian National University for the purposes of duplicate question detection in educational forums. A state of the art neural network model is trained on the dataset to measure its usefulness. An accuracy of 91.8% is achieved, which is on par with what is achieved on other datasets with similar features. A high performing neural network for this dataset could potentially be used to create a live system that detects and reuses answers for duplicate questions on course forums.