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.
E-health technologies have the potential to provide scalable and accessible interventions for youth mental health. As part of a developing an ecosystem of e-screening and e-therapy tools for New Zealand young people, a dialog agent, Headstrong, has been designed to promote resilience with methods grounded in cognitive behavioral therapy and positive psychology. This paper describes the architecture underlying the chatbot. The architecture supports a range of over 20 activities delivered in a 4-week program by relatable personas. The architecture provides a visual authoring interface to its content management system. In addition to supporting the original adolescent resilience chatbot, the architecture has been reused to create a 3-week 'stress-detox' intervention for undergraduates, and subsequently for a chatbot to support young people with the impacts of the COVID-19 pandemic, with all three systems having been used in field trials. The Headstrong architecture illustrates the feasibility of creating a domain-focused authoring environment in the context of e-therapy that supports non-technical expert input and rapid deployment.