Proceedings of the Annual Meeting of the Cognitive Science Society

Hundreds of millions of people learn a second language (L2).1When learning a specific L2, there are common errors for na-tive speakers of a given L1 language, suggesting specific ef-fects of L1 on L2 learning. Nevertheless, language instruc-tion materials are designed based only on L2. We developa computational model that mimics the behavior of a non-native speaker of a specific language to provide a deeper un-derstanding of the problem of learning a second language. Weuse a Naive Bayes to model prepositional choices in English(L2) by native Mandarin (L1) speakers. Our results show thatboth correct and incorrect responses can be explained by thelearner’s L1 information. Moreover, our model predicts incor-rect choices with no explicit training data of non-native mis-takes. Our results thus provide a new medium to analyze anddevelop tools for L2 teaching.