The utilization of virtual reality (VR) in the field of language learning is a hot issue for both educational practitioners and researchers. This research summarized the empirical studies on applying VR in language learning from 2011 to 2020. The authors analyzed a total of 67 empirical studies in twelve peer-reviewed journals. On the basis of the coding framework, we used content analysis for further data analysis. The current study revealed the general research status, major research themes of the empirical studies and the guiding theories for the instructional design with VR. The advantages and challenges of integrating VR to language learning were summarized in the end.

In this paper, we propose a clustering-based online news topic detection and tracking (TDT) approach based on hierarchical Bayesian nonparametric framework that allows topics to be shared across different news stories in a corpus. Our approach is formulated using the hierarchical Pitman-Yor process mixture model with the inverted Beta-Liouville (IBL) distribution as its component density, which has shown superior performance in modeling text data than the widely used Gaussian distribution. Moreover, we theoretically develop a convergence-guaranteed online learning algorithm that can effectively learn the proposed TDT model from a stream of news stories based on varational Bayes. The merits of our TDT approach are illustrated by comparing it with other well-defined clustering-based TDT approaches on different news data sets.