@article{Ni_Bao_Li_Qi_Denny_Warren_Witbrock_Liu_2022, title={DeepQR: Neural-Based Quality Ratings for Learnersourced Multiple-Choice Questions}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/21562}, DOI={10.1609/aaai.v36i11.21562}, abstractNote={Automated question quality rating (AQQR) aims to evaluate question quality through computational means, thereby addressing emerging challenges in online learnersourced question repositories. Existing methods for AQQR rely solely on explicitly-defined criteria such as readability and word count, while not fully utilising the power of state-of-the-art deep-learning techniques. We propose DeepQR, a novel neural-network model for AQQR that is trained using multiple-choice-question (MCQ) datasets collected from PeerWise, a widely-used learnersourcing platform. Along with designing DeepQR, we investigate models based on explicitly-defined features, or semantic features, or both. We also introduce a self-attention mechanism to capture semantic correlations between MCQ components, and a contrastive-learning approach to acquire question representations using quality ratings. Extensive experiments on datasets collected from eight university-level courses illustrate that DeepQR has superior performance over six comparative models.}, number={11}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ni, Lin and Bao, Qiming and Li, Xiaoxuan and Qi, Qianqian and Denny, Paul and Warren, Jim and Witbrock, Michael and Liu, Jiamou}, year={2022}, month={Jun.}, pages={12826-12834} }