No abstract

Amidst rising mental health needs in society, virtual agents are increasingly deployed in counselling. In order to give pertinent advice, counsellors must first gain an understanding of the issues at hand by eliciting sharing from the counsellee. It is thus important for the counsellor chatbot to encourage the user to open up and talk. One way to sustain the conversation flow is to acknowledge the counsellee's key points by restating them, or probing them further with questions. This paper applies models from two closely related NLP tasks -summarization and question generation -to restatement and question generation in the counselling context. We conducted experiments on a manually annotated dataset of Cantonese post-reply pairs on topics related to loneliness, academic anxiety and test anxiety. We obtained the best performance in both restatement and question generation by finetuning BertSum, a state-of-the-art summarization model, with the in-domain manual dataset augmented with a large-scale, automatically mined open-domain dataset.

This systematic review explores test anxiety in children and young people (CYP) with learning difficulties. Research has found that students with learning difficulties experience higher levels of anxiety about school compared to peers without learning difficulties. One area of school that has had little research is test anxiety, therefore further exploration is needed. Nine papers resulted from the systematic search. It was found that CYP with learning difficulties can experience test anxiety. There are a variety of internal and external factors which have an interactional relationship with test anxiety. A model was developed to illustrate these factors including characteristics of tests, perceptions of support, self-belief and cognition and learning skills. This can be used to explore potential reasons for test anxiety in CYP with learning difficulties and to provide subsequent support.