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IntroductionThis study examines the emotional support offered by the non-player characters (NPCs) in an interactive learning environment, as well as the effects of the perceived playfulness of the interactive system on German language learning.MethodWe developed a role-playing library system to serve this purpose. 2,377 Chinese Internet users were surveyed using online questionnaire.ResultsA theoretical model of emotion- driven learning (ELM) was proposed based on the analysis results of valid recovered data. Additionally, NPCs were found to be effective in improving learning outcomes through emotional support.DiscussionAn interactive education system may be able to enhance the perceived playfulness of learning in order to enhance the learning experience.

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Precisely predicting the drug–drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.

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