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.
@article{arxiv.2111.10058,
title = {DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions},
author = {Lin Ni and Qiming Bao and Xiaoxuan Li and Qianqian Qi and Paul Denny and Jim Warren and Michael Witbrock and Jiamou Liu},
journal= {arXiv preprint arXiv:2111.10058},
year = {2021}
}