Visualization Analytics for Second Language Vocabulary Learning in Virtual Worlds

Abstract

Language learning occurring in authentic contexts has been shown to be more effective. Virtual worlds provide simulated contexts that have the necessary elements of authentic contexts for language learning, and as a result, many studies have adopted virtual worlds as a useful platform for language learning. However, few studies so far have examined the relationship between learning outcomes and learning paths and strategies inside a virtual world. This study was designed to fill this research gap. In order to understand the impact of different learning strategies on learning outcomes in a virtual world, a visualization analytic method was developed to examine the recorded learner paths within a virtual world while learning occurred. In particular, the visualization analytic method adopted in this study was based on social network analysis. This study included 14 participants who were learners of Mandarin Chinese as a foreign language. The learning outcomes were based on their test scores from 7 learning sessions and the post-test. Through the visualization analysis, the current study revealed a link between the learning paths and strategies and learners’ outcomes. The strategies include the “nearest strategy,” the “focus strategy,” and the “cluster strategy.” The findings show that high-achieving and low-achieving students tend to use different strategies in learning new words. The visualization analytics thus effectively displays the learning strategies of vocabulary acquisition. Our methods could be applied to other second language learning studies, and the results can also provide insights into the construction of future virtual worlds for learning second languages.