Learning Graph Representation for Predicting Student Mental Wellbeing in Robot Assisted Journal Writing Context

Conversational agents are increasingly introduced in mental health and well-being research. One promising application is in assessing students' mental well-being while facilitating self-disclosure activities such as administering questionnaires or assisting in journaling. In these applications, the data collected is mostly multimodal and strategies for fully harnessing the potential of this data is an open challenge. Moreover, most of the machine learning (ML) research within healthcare has been carried out using conventional, baseline models which fail to capitalise on the inherent graphical characteristics of health data. Learning from graph representations encompasses techniques such as feature propagation and aggregation methods to combine feature information and graph structure in order to learn better data representations. Therefore, in this paper we propose a novel graph representation learning strategy to predict student mental health status from multimodal data in a robot assisted journal writing context. Twenty undergraduate / graduate students interacted with the Pepper robot in a dyadic interaction setting over four weeks (one session per week). Graph representation learning was applied on their questionnaire data (mental wellbeing state, mood changes, perceptions toward Pepper, and motivational state) and physiological data (electro-cardiogram). Comparative experimental results demonstrate that graph representation learning based classification enables mental wellbeing assessment with an accuracy of 92.5% which is superior to standard ML methods that do not take into account graphical structures.