  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.
